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<meta name="author" content="Brenna Armstrong, Tiffany D. Barnes, Daina Chiba, and Diana Z. O’Brien" />


<title>Replication code for “Financial Crises and the Selection and Survival of Women Finance Ministers” American Political Science Review</title>

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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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t=V(this),e=t.uniqueId().attr("id"),i=t.next(),s=i.uniqueId().attr("id");t.attr("aria-controls",s),i.attr("aria-labelledby",e)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(t.event),"fill"===e?(i=s.height(),this.element.siblings(":visible").each(function(){var t=V(this),e=t.css("position");"absolute"!==e&&"fixed"!==e&&(i-=t.outerHeight(!0))}),this.headers.each(function(){i-=V(this).outerHeight(!0)}),this.headers.next().each(function(){V(this).height(Math.max(0,i-V(this).innerHeight()+V(this).height()))}).css("overflow","auto")):"auto"===e&&(i=0,this.headers.next().each(function(){var t=V(this).is(":visible");t||V(this).show(),i=Math.max(i,V(this).css("height","").height()),t||V(this).hide()}).height(i))},_activate:function(t){t=this._findActive(t)[0];t!==this.active[0]&&(t=t||this.active[0],this._eventHandler({target:t,currentTarget:t,preventDefault:V.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):V()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&V.each(t.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var 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e=t.newPanel,i=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=e,this.prevHide=i,this.options.animate?this._animate(e,i,t):(i.hide(),e.show(),this._toggleComplete(t)),i.attr({"aria-hidden":"true"}),i.prev().attr({"aria-selected":"false","aria-expanded":"false"}),e.length&&i.length?i.prev().attr({tabIndex:-1,"aria-expanded":"false"}):e.length&&this.headers.filter(function(){return 0===parseInt(V(this).attr("tabIndex"),10)}).attr("tabIndex",-1),e.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(t,i,e){var s,n,o,a=this,r=0,l=t.css("box-sizing"),h=t.length&&(!i.length||t.index()<i.index()),c=this.options.animate||{},u=h&&c.down||c,h=function(){a._toggleComplete(e)};return n=(n="string"==typeof u?u:n)||u.easing||c.easing,o=(o="number"==typeof u?u:o)||u.duration||c.duration,i.length?t.length?(s=t.show().outerHeight(),i.animate(this.hideProps,{duration:o,easing:n,step:function(t,e){e.now=Math.round(t)}}),void t.hide().animate(this.showProps,{duration:o,easing:n,complete:h,step:function(t,e){e.now=Math.round(t),"height"!==e.prop?"content-box"===l&&(r+=e.now):"content"!==a.options.heightStyle&&(e.now=Math.round(s-i.outerHeight()-r),r=0)}})):i.animate(this.hideProps,o,n,h):t.animate(this.showProps,o,n,h)},_toggleComplete:function(t){var e=t.oldPanel,i=e.prev();this._removeClass(e,"ui-accordion-content-active"),this._removeClass(i,"ui-accordion-header-active")._addClass(i,"ui-accordion-header-collapsed"),e.length&&(e.parent()[0].className=e.parent()[0].className),this._trigger("activate",null,t)}}),V.ui.safeActiveElement=function(e){var i;try{i=e.activeElement}catch(t){i=e.body}return i=!(i=i||e.body).nodeName?e.body:i},V.widget("ui.menu",{version:"1.13.2",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-caret-1-e"},items:"> *",menus:"ul",position:{my:"left top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.lastMousePosition={x:null,y:null},this.element.uniqueId().attr({role:this.options.role,tabIndex:0}),this._addClass("ui-menu","ui-widget ui-widget-content"),this._on({"mousedown .ui-menu-item":function(t){t.preventDefault(),this._activateItem(t)},"click .ui-menu-item":function(t){var e=V(t.target),i=V(V.ui.safeActiveElement(this.document[0]));!this.mouseHandled&&e.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),e.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&i.closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":"_activateItem","mousemove .ui-menu-item":"_activateItem",mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(t,e){var i=this.active||this._menuItems().first();e||this.focus(t,i)},blur:function(t){this._delay(function(){V.contains(this.element[0],V.ui.safeActiveElement(this.document[0]))||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(t){this._closeOnDocumentClick(t)&&this.collapseAll(t,!0),this.mouseHandled=!1}})},_activateItem:function(t){var e,i;this.previousFilter||t.clientX===this.lastMousePosition.x&&t.clientY===this.lastMousePosition.y||(this.lastMousePosition={x:t.clientX,y:t.clientY},e=V(t.target).closest(".ui-menu-item"),i=V(t.currentTarget),e[0]===i[0]&&(i.is(".ui-state-active")||(this._removeClass(i.siblings().children(".ui-state-active"),null,"ui-state-active"),this.focus(t,i))))},_destroy:function(){var t=this.element.find(".ui-menu-item").removeAttr("role aria-disabled").children(".ui-menu-item-wrapper").removeUniqueId().removeAttr("tabIndex role aria-haspopup");this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeAttr("role aria-labelledby aria-expanded aria-hidden aria-disabled tabIndex").removeUniqueId().show(),t.children().each(function(){var t=V(this);t.data("ui-menu-submenu-caret")&&t.remove()})},_keydown:function(t){var e,i,s,n=!0;switch(t.keyCode){case V.ui.keyCode.PAGE_UP:this.previousPage(t);break;case V.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case V.ui.keyCode.HOME:this._move("first","first",t);break;case V.ui.keyCode.END:this._move("last","last",t);break;case V.ui.keyCode.UP:this.previous(t);break;case V.ui.keyCode.DOWN:this.next(t);break;case V.ui.keyCode.LEFT:this.collapse(t);break;case V.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case V.ui.keyCode.ENTER:case V.ui.keyCode.SPACE:this._activate(t);break;case V.ui.keyCode.ESCAPE:this.collapse(t);break;default:e=this.previousFilter||"",s=n=!1,i=96<=t.keyCode&&t.keyCode<=105?(t.keyCode-96).toString():String.fromCharCode(t.keyCode),clearTimeout(this.filterTimer),i===e?s=!0:i=e+i,e=this._filterMenuItems(i),(e=s&&-1!==e.index(this.active.next())?this.active.nextAll(".ui-menu-item"):e).length||(i=String.fromCharCode(t.keyCode),e=this._filterMenuItems(i)),e.length?(this.focus(t,e),this.previousFilter=i,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}n&&t.preventDefault()},_activate:function(t){this.active&&!this.active.is(".ui-state-disabled")&&(this.active.children("[aria-haspopup='true']").length?this.expand(t):this.select(t))},refresh:function(){var t,e,s=this,n=this.options.icons.submenu,i=this.element.find(this.options.menus);this._toggleClass("ui-menu-icons",null,!!this.element.find(".ui-icon").length),e=i.filter(":not(.ui-menu)").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=V(this),e=t.prev(),i=V("<span>").data("ui-menu-submenu-caret",!0);s._addClass(i,"ui-menu-icon","ui-icon "+n),e.attr("aria-haspopup","true").prepend(i),t.attr("aria-labelledby",e.attr("id"))}),this._addClass(e,"ui-menu","ui-widget ui-widget-content ui-front"),(t=i.add(this.element).find(this.options.items)).not(".ui-menu-item").each(function(){var t=V(this);s._isDivider(t)&&s._addClass(t,"ui-menu-divider","ui-widget-content")}),i=(e=t.not(".ui-menu-item, .ui-menu-divider")).children().not(".ui-menu").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),this._addClass(e,"ui-menu-item")._addClass(i,"ui-menu-item-wrapper"),t.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!V.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(t,e){var i;"icons"===t&&(i=this.element.find(".ui-menu-icon"),this._removeClass(i,null,this.options.icons.submenu)._addClass(i,null,e.submenu)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",String(t)),this._toggleClass(null,"ui-state-disabled",!!t)},focus:function(t,e){var i;this.blur(t,t&&"focus"===t.type),this._scrollIntoView(e),this.active=e.first(),i=this.active.children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",i.attr("id")),i=this.active.parent().closest(".ui-menu-item").children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),t&&"keydown"===t.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),(i=e.children(".ui-menu")).length&&t&&/^mouse/.test(t.type)&&this._startOpening(i),this.activeMenu=e.parent(),this._trigger("focus",t,{item:e})},_scrollIntoView:function(t){var e,i,s;this._hasScroll()&&(i=parseFloat(V.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(V.css(this.activeMenu[0],"paddingTop"))||0,e=t.offset().top-this.activeMenu.offset().top-i-s,i=this.activeMenu.scrollTop(),s=this.activeMenu.height(),t=t.outerHeight(),e<0?this.activeMenu.scrollTop(i+e):s<e+t&&this.activeMenu.scrollTop(i+e-s+t))},blur:function(t,e){e||clearTimeout(this.timer),this.active&&(this._removeClass(this.active.children(".ui-menu-item-wrapper"),null,"ui-state-active"),this._trigger("blur",t,{item:this.active}),this.active=null)},_startOpening:function(t){clearTimeout(this.timer),"true"===t.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(t)},this.delay))},_open:function(t){var e=V.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(e)},collapseAll:function(e,i){clearTimeout(this.timer),this.timer=this._delay(function(){var t=i?this.element:V(e&&e.target).closest(this.element.find(".ui-menu"));t.length||(t=this.element),this._close(t),this.blur(e),this._removeClass(t.find(".ui-state-active"),null,"ui-state-active"),this.activeMenu=t},i?0:this.delay)},_close:function(t){(t=t||(this.active?this.active.parent():this.element)).find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false")},_closeOnDocumentClick:function(t){return!V(t.target).closest(".ui-menu").length},_isDivider:function(t){return!/[^\-\u2014\u2013\s]/.test(t.text())},collapse:function(t){var e=this.active&&this.active.parent().closest(".ui-menu-item",this.element);e&&e.length&&(this._close(),this.focus(t,e))},expand:function(t){var e=this.active&&this._menuItems(this.active.children(".ui-menu")).first();e&&e.length&&(this._open(e.parent()),this._delay(function(){this.focus(t,e)}))},next:function(t){this._move("next","first",t)},previous:function(t){this._move("prev","last",t)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_menuItems:function(t){return(t||this.element).find(this.options.items).filter(".ui-menu-item")},_move:function(t,e,i){var s;(s=this.active?"first"===t||"last"===t?this.active["first"===t?"prevAll":"nextAll"](".ui-menu-item").last():this.active[t+"All"](".ui-menu-item").first():s)&&s.length&&this.active||(s=this._menuItems(this.activeMenu)[e]()),this.focus(i,s)},nextPage:function(t){var e,i,s;this.active?this.isLastItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.nextAll(".ui-menu-item").each(function(){return(e=V(this)).offset().top-i-s<0}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu)[this.active?"last":"first"]())):this.next(t)},previousPage:function(t){var e,i,s;this.active?this.isFirstItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.prevAll(".ui-menu-item").each(function(){return 0<(e=V(this)).offset().top-i+s}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu).first())):this.next(t)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||V(t.target).closest(".ui-menu-item");var e={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,e)},_filterMenuItems:function(t){var t=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),e=new RegExp("^"+t,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return e.test(String.prototype.trim.call(V(this).children(".ui-menu-item-wrapper").text()))})}});V.widget("ui.autocomplete",{version:"1.13.2",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,liveRegionTimer:null,_create:function(){var i,s,n,t=this.element[0].nodeName.toLowerCase(),e="textarea"===t,t="input"===t;this.isMultiLine=e||!t&&this._isContentEditable(this.element),this.valueMethod=this.element[e||t?"val":"text"],this.isNewMenu=!0,this._addClass("ui-autocomplete-input"),this.element.attr("autocomplete","off"),this._on(this.element,{keydown:function(t){if(this.element.prop("readOnly"))s=n=i=!0;else{s=n=i=!1;var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:i=!0,this._move("previousPage",t);break;case e.PAGE_DOWN:i=!0,this._move("nextPage",t);break;case e.UP:i=!0,this._keyEvent("previous",t);break;case e.DOWN:i=!0,this._keyEvent("next",t);break;case e.ENTER:this.menu.active&&(i=!0,t.preventDefault(),this.menu.select(t));break;case e.TAB:this.menu.active&&this.menu.select(t);break;case e.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(t),t.preventDefault());break;default:s=!0,this._searchTimeout(t)}}},keypress:function(t){if(i)return i=!1,void(this.isMultiLine&&!this.menu.element.is(":visible")||t.preventDefault());if(!s){var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:this._move("previousPage",t);break;case e.PAGE_DOWN:this._move("nextPage",t);break;case e.UP:this._keyEvent("previous",t);break;case e.DOWN:this._keyEvent("next",t)}}},input:function(t){if(n)return n=!1,void t.preventDefault();this._searchTimeout(t)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){clearTimeout(this.searching),this.close(t),this._change(t)}}),this._initSource(),this.menu=V("<ul>").appendTo(this._appendTo()).menu({role:null}).hide().attr({unselectable:"on"}).menu("instance"),this._addClass(this.menu.element,"ui-autocomplete","ui-front"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault()},menufocus:function(t,e){var i,s;if(this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type)))return this.menu.blur(),void this.document.one("mousemove",function(){V(t.target).trigger(t.originalEvent)});s=e.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:s})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(s.value),(i=e.item.attr("aria-label")||s.value)&&String.prototype.trim.call(i).length&&(clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(i))},100))},menuselect:function(t,e){var i=e.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==V.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",t,{item:i})&&this._value(i.value),this.term=this._value(),this.close(t),this.selectedItem=i}}),this.liveRegion=V("<div>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(t){var e=this.menu.element[0];return t.target===this.element[0]||t.target===e||V.contains(e,t.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_initSource:function(){var i,s,n=this;Array.isArray(this.options.source)?(i=this.options.source,this.source=function(t,e){e(V.ui.autocomplete.filter(i,t.term))}):"string"==typeof this.options.source?(s=this.options.source,this.source=function(t,e){n.xhr&&n.xhr.abort(),n.xhr=V.ajax({url:s,data:t,dataType:"json",success:function(t){e(t)},error:function(){e([])}})}):this.source=this.options.source},_searchTimeout:function(s){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),e=this.menu.element.is(":visible"),i=s.altKey||s.ctrlKey||s.metaKey||s.shiftKey;t&&(e||i)||(this.selectedItem=null,this.search(null,s))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length<this.options.minLength?this.close(e):!1!==this._trigger("search",e)?this._search(t):void 0},_search:function(t){this.pending++,this._addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:t},this._response())},_response:function(){var e=++this.requestIndex;return function(t){e===this.requestIndex&&this.__response(t),this.pending--,this.pending||this._removeClass("ui-autocomplete-loading")}.bind(this)},__response:function(t){t=t&&this._normalize(t),this._trigger("response",null,{content:t}),!this.options.disabled&&t&&t.length&&!this.cancelSearch?(this._suggest(t),this._trigger("open")):this._close()},close:function(t){this.cancelSearch=!0,this._close(t)},_close:function(t){this._off(this.document,"mousedown"),this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",t))},_change:function(t){this.previous!==this._value()&&this._trigger("change",t,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:V.map(t,function(t){return"string"==typeof t?{label:t,value:t}:V.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var e=this.menu.element.empty();this._renderMenu(e,t),this.isNewMenu=!0,this.menu.refresh(),e.show(),this._resizeMenu(),e.position(V.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next(),this._on(this.document,{mousedown:"_closeOnClickOutside"})},_resizeMenu:function(){var t=this.menu.element;t.outerWidth(Math.max(t.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(i,t){var s=this;V.each(t,function(t,e){s._renderItemData(i,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-autocomplete-item",e)},_renderItem:function(t,e){return V("<li>").append(V("<div>").text(e.label)).appendTo(t)},_move:function(t,e){if(this.menu.element.is(":visible"))return this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),void this.menu.blur()):void this.menu[t](e);this.search(null,e)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){this.isMultiLine&&!this.menu.element.is(":visible")||(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),V.extend(V.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,e){var i=new RegExp(V.ui.autocomplete.escapeRegex(e),"i");return V.grep(t,function(t){return i.test(t.label||t.value||t)})}}),V.widget("ui.autocomplete",V.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(1<t?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var e;this._superApply(arguments),this.options.disabled||this.cancelSearch||(e=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(e))},100))}});V.ui.autocomplete;var tt=/ui-corner-([a-z]){2,6}/g;V.widget("ui.controlgroup",{version:"1.13.2",defaultElement:"<div>",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var o=this,a=[];V.each(this.options.items,function(s,t){var e,n={};if(t)return"controlgroupLabel"===s?((e=o.element.find(t)).each(function(){var t=V(this);t.children(".ui-controlgroup-label-contents").length||t.contents().wrapAll("<span class='ui-controlgroup-label-contents'></span>")}),o._addClass(e,null,"ui-widget ui-widget-content ui-state-default"),void(a=a.concat(e.get()))):void(V.fn[s]&&(n=o["_"+s+"Options"]?o["_"+s+"Options"]("middle"):{classes:{}},o.element.find(t).each(function(){var t=V(this),e=t[s]("instance"),i=V.widget.extend({},n);"button"===s&&t.parent(".ui-spinner").length||((e=e||t[s]()[s]("instance"))&&(i.classes=o._resolveClassesValues(i.classes,e)),t[s](i),i=t[s]("widget"),V.data(i[0],"ui-controlgroup-data",e||t[s]("instance")),a.push(i[0]))})))}),this.childWidgets=V(V.uniqueSort(a)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var t=V(this).data("ui-controlgroup-data");t&&t[e]&&t[e]()})},_updateCornerClass:function(t,e){e=this._buildSimpleOptions(e,"label").classes.label;this._removeClass(t,null,"ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right ui-corner-all"),this._addClass(t,null,e)},_buildSimpleOptions:function(t,e){var i="vertical"===this.options.direction,s={classes:{}};return s.classes[e]={middle:"",first:"ui-corner-"+(i?"top":"left"),last:"ui-corner-"+(i?"bottom":"right"),only:"ui-corner-all"}[t],s},_spinnerOptions:function(t){t=this._buildSimpleOptions(t,"ui-spinner");return t.classes["ui-spinner-up"]="",t.classes["ui-spinner-down"]="",t},_buttonOptions:function(t){return this._buildSimpleOptions(t,"ui-button")},_checkboxradioOptions:function(t){return this._buildSimpleOptions(t,"ui-checkboxradio-label")},_selectmenuOptions:function(t){var e="vertical"===this.options.direction;return{width:e&&"auto",classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(i,s){var n={};return V.each(i,function(t){var e=s.options.classes[t]||"",e=String.prototype.trim.call(e.replace(tt,""));n[t]=(e+" "+i[t]).replace(/\s+/g," ")}),n},_setOption:function(t,e){"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"!==t?this.refresh():this._callChildMethod(e?"disable":"enable")},refresh:function(){var n,o=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),n=this.childWidgets,(n=this.options.onlyVisible?n.filter(":visible"):n).length&&(V.each(["first","last"],function(t,e){var i,s=n[e]().data("ui-controlgroup-data");s&&o["_"+s.widgetName+"Options"]?((i=o["_"+s.widgetName+"Options"](1===n.length?"only":e)).classes=o._resolveClassesValues(i.classes,s),s.element[s.widgetName](i)):o._updateCornerClass(n[e](),e)}),this._callChildMethod("refresh"))}});V.widget("ui.checkboxradio",[V.ui.formResetMixin,{version:"1.13.2",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var t,e=this._super()||{};return this._readType(),t=this.element.labels(),this.label=V(t[t.length-1]),this.label.length||V.error("No label found for checkboxradio widget"),this.originalLabel="",(t=this.label.contents().not(this.element[0])).length&&(this.originalLabel+=t.clone().wrapAll("<div></div>").parent().html()),this.originalLabel&&(e.label=this.originalLabel),null!=(t=this.element[0].disabled)&&(e.disabled=t),e},_create:function(){var t=this.element[0].checked;this._bindFormResetHandler(),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled),this._setOption("disabled",this.options.disabled),this._addClass("ui-checkboxradio","ui-helper-hidden-accessible"),this._addClass(this.label,"ui-checkboxradio-label","ui-button ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var t=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===t&&/radio|checkbox/.test(this.type)||V.error("Can't create checkboxradio on element.nodeName="+t+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var t=this.element[0].name,e="input[name='"+V.escapeSelector(t)+"']";return t?(this.form.length?V(this.form[0].elements).filter(e):V(e).filter(function(){return 0===V(this)._form().length})).not(this.element):V([])},_toggleClasses:function(){var t=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",t)._toggleClass(this.icon,null,"ui-icon-blank",!t),"radio"===this.type&&this._getRadioGroup().each(function(){var t=V(this).checkboxradio("instance");t&&t._removeClass(t.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){if("label"!==t||e){if(this._super(t,e),"disabled"===t)return this._toggleClass(this.label,null,"ui-state-disabled",e),void(this.element[0].disabled=e);this.refresh()}},_updateIcon:function(t){var e="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=V("<span>"),this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(e+=t?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,t?"ui-icon-blank":"ui-icon-check")):e+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",e),t||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),(t=this.iconSpace?t.not(this.iconSpace[0]):t).remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]);var et;V.ui.checkboxradio;V.widget("ui.button",{version:"1.13.2",defaultElement:"<button>",options:{classes:{"ui-button":"ui-corner-all"},disabled:null,icon:null,iconPosition:"beginning",label:null,showLabel:!0},_getCreateOptions:function(){var t,e=this._super()||{};return this.isInput=this.element.is("input"),null!=(t=this.element[0].disabled)&&(e.disabled=t),this.originalLabel=this.isInput?this.element.val():this.element.html(),this.originalLabel&&(e.label=this.originalLabel),e},_create:function(){!this.option.showLabel&!this.options.icon&&(this.options.showLabel=!0),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled||!1),this.hasTitle=!!this.element.attr("title"),this.options.label&&this.options.label!==this.originalLabel&&(this.isInput?this.element.val(this.options.label):this.element.html(this.options.label)),this._addClass("ui-button","ui-widget"),this._setOption("disabled",this.options.disabled),this._enhance(),this.element.is("a")&&this._on({keyup:function(t){t.keyCode===V.ui.keyCode.SPACE&&(t.preventDefault(),this.element[0].click?this.element[0].click():this.element.trigger("click"))}})},_enhance:function(){this.element.is("button")||this.element.attr("role","button"),this.options.icon&&(this._updateIcon("icon",this.options.icon),this._updateTooltip())},_updateTooltip:function(){this.title=this.element.attr("title"),this.options.showLabel||this.title||this.element.attr("title",this.options.label)},_updateIcon:function(t,e){var i="iconPosition"!==t,s=i?this.options.iconPosition:e,t="top"===s||"bottom"===s;this.icon?i&&this._removeClass(this.icon,null,this.options.icon):(this.icon=V("<span>"),this._addClass(this.icon,"ui-button-icon","ui-icon"),this.options.showLabel||this._addClass("ui-button-icon-only")),i&&this._addClass(this.icon,null,e),this._attachIcon(s),t?(this._addClass(this.icon,null,"ui-widget-icon-block"),this.iconSpace&&this.iconSpace.remove()):(this.iconSpace||(this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-button-icon-space")),this._removeClass(this.icon,null,"ui-wiget-icon-block"),this._attachIconSpace(s))},_destroy:function(){this.element.removeAttr("role"),this.icon&&this.icon.remove(),this.iconSpace&&this.iconSpace.remove(),this.hasTitle||this.element.removeAttr("title")},_attachIconSpace:function(t){this.icon[/^(?:end|bottom)/.test(t)?"before":"after"](this.iconSpace)},_attachIcon:function(t){this.element[/^(?:end|bottom)/.test(t)?"append":"prepend"](this.icon)},_setOptions:function(t){var e=(void 0===t.showLabel?this.options:t).showLabel,i=(void 0===t.icon?this.options:t).icon;e||i||(t.showLabel=!0),this._super(t)},_setOption:function(t,e){"icon"===t&&(e?this._updateIcon(t,e):this.icon&&(this.icon.remove(),this.iconSpace&&this.iconSpace.remove())),"iconPosition"===t&&this._updateIcon(t,e),"showLabel"===t&&(this._toggleClass("ui-button-icon-only",null,!e),this._updateTooltip()),"label"===t&&(this.isInput?this.element.val(e):(this.element.html(e),this.icon&&(this._attachIcon(this.options.iconPosition),this._attachIconSpace(this.options.iconPosition)))),this._super(t,e),"disabled"===t&&(this._toggleClass(null,"ui-state-disabled",e),(this.element[0].disabled=e)&&this.element.trigger("blur"))},refresh:function(){var t=this.element.is("input, button")?this.element[0].disabled:this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOptions({disabled:t}),this._updateTooltip()}}),!1!==V.uiBackCompat&&(V.widget("ui.button",V.ui.button,{options:{text:!0,icons:{primary:null,secondary:null}},_create:function(){this.options.showLabel&&!this.options.text&&(this.options.showLabel=this.options.text),!this.options.showLabel&&this.options.text&&(this.options.text=this.options.showLabel),this.options.icon||!this.options.icons.primary&&!this.options.icons.secondary?this.options.icon&&(this.options.icons.primary=this.options.icon):this.options.icons.primary?this.options.icon=this.options.icons.primary:(this.options.icon=this.options.icons.secondary,this.options.iconPosition="end"),this._super()},_setOption:function(t,e){"text"!==t?("showLabel"===t&&(this.options.text=e),"icon"===t&&(this.options.icons.primary=e),"icons"===t&&(e.primary?(this._super("icon",e.primary),this._super("iconPosition","beginning")):e.secondary&&(this._super("icon",e.secondary),this._super("iconPosition","end"))),this._superApply(arguments)):this._super("showLabel",e)}}),V.fn.button=(et=V.fn.button,function(i){var t="string"==typeof i,s=Array.prototype.slice.call(arguments,1),n=this;return t?this.length||"instance"!==i?this.each(function(){var t=V(this).attr("type"),e=V.data(this,"ui-"+("checkbox"!==t&&"radio"!==t?"button":"checkboxradio"));return"instance"===i?(n=e,!1):e?"function"!=typeof e[i]||"_"===i.charAt(0)?V.error("no such method '"+i+"' for button widget instance"):(t=e[i].apply(e,s))!==e&&void 0!==t?(n=t&&t.jquery?n.pushStack(t.get()):t,!1):void 0:V.error("cannot call methods on button prior to initialization; attempted to call method '"+i+"'")}):n=void 0:(s.length&&(i=V.widget.extend.apply(null,[i].concat(s))),this.each(function(){var t=V(this).attr("type"),e="checkbox"!==t&&"radio"!==t?"button":"checkboxradio",t=V.data(this,"ui-"+e);t?(t.option(i||{}),t._init&&t._init()):"button"!=e?V(this).checkboxradio(V.extend({icon:!1},i)):et.call(V(this),i)})),n}),V.fn.buttonset=function(){return V.ui.controlgroup||V.error("Controlgroup widget missing"),"option"===arguments[0]&&"items"===arguments[1]&&arguments[2]?this.controlgroup.apply(this,[arguments[0],"items.button",arguments[2]]):"option"===arguments[0]&&"items"===arguments[1]?this.controlgroup.apply(this,[arguments[0],"items.button"]):("object"==typeof arguments[0]&&arguments[0].items&&(arguments[0].items={button:arguments[0].items}),this.controlgroup.apply(this,arguments))});var it;V.ui.button;function st(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:"",selectMonthLabel:"Select month",selectYearLabel:"Select year"},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,onUpdateDatepicker:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},V.extend(this._defaults,this.regional[""]),this.regional.en=V.extend(!0,{},this.regional[""]),this.regional["en-US"]=V.extend(!0,{},this.regional.en),this.dpDiv=nt(V("<div id='"+this._mainDivId+"' class='ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>"))}function nt(t){var e="button, .ui-datepicker-prev, .ui-datepicker-next, .ui-datepicker-calendar td a";return t.on("mouseout",e,function(){V(this).removeClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).removeClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).removeClass("ui-datepicker-next-hover")}).on("mouseover",e,ot)}function ot(){V.datepicker._isDisabledDatepicker((it.inline?it.dpDiv.parent():it.input)[0])||(V(this).parents(".ui-datepicker-calendar").find("a").removeClass("ui-state-hover"),V(this).addClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).addClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).addClass("ui-datepicker-next-hover"))}function at(t,e){for(var i in V.extend(t,e),e)null==e[i]&&(t[i]=e[i]);return t}V.extend(V.ui,{datepicker:{version:"1.13.2"}}),V.extend(st.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(t){return at(this._defaults,t||{}),this},_attachDatepicker:function(t,e){var i,s=t.nodeName.toLowerCase(),n="div"===s||"span"===s;t.id||(this.uuid+=1,t.id="dp"+this.uuid),(i=this._newInst(V(t),n)).settings=V.extend({},e||{}),"input"===s?this._connectDatepicker(t,i):n&&this._inlineDatepicker(t,i)},_newInst:function(t,e){return{id:t[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1"),input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:e,dpDiv:e?nt(V("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(t,e){var i=V(t);e.append=V([]),e.trigger=V([]),i.hasClass(this.markerClassName)||(this._attachments(i,e),i.addClass(this.markerClassName).on("keydown",this._doKeyDown).on("keypress",this._doKeyPress).on("keyup",this._doKeyUp),this._autoSize(e),V.data(t,"datepicker",e),e.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,e){var i,s=this._get(e,"appendText"),n=this._get(e,"isRTL");e.append&&e.append.remove(),s&&(e.append=V("<span>").addClass(this._appendClass).text(s),t[n?"before":"after"](e.append)),t.off("focus",this._showDatepicker),e.trigger&&e.trigger.remove(),"focus"!==(i=this._get(e,"showOn"))&&"both"!==i||t.on("focus",this._showDatepicker),"button"!==i&&"both"!==i||(s=this._get(e,"buttonText"),i=this._get(e,"buttonImage"),this._get(e,"buttonImageOnly")?e.trigger=V("<img>").addClass(this._triggerClass).attr({src:i,alt:s,title:s}):(e.trigger=V("<button type='button'>").addClass(this._triggerClass),i?e.trigger.html(V("<img>").attr({src:i,alt:s,title:s})):e.trigger.text(s)),t[n?"before":"after"](e.trigger),e.trigger.on("click",function(){return V.datepicker._datepickerShowing&&V.datepicker._lastInput===t[0]?V.datepicker._hideDatepicker():(V.datepicker._datepickerShowing&&V.datepicker._lastInput!==t[0]&&V.datepicker._hideDatepicker(),V.datepicker._showDatepicker(t[0])),!1}))},_autoSize:function(t){var e,i,s,n,o,a;this._get(t,"autoSize")&&!t.inline&&(o=new Date(2009,11,20),(a=this._get(t,"dateFormat")).match(/[DM]/)&&(e=function(t){for(n=s=i=0;n<t.length;n++)t[n].length>i&&(i=t[n].length,s=n);return s},o.setMonth(e(this._get(t,a.match(/MM/)?"monthNames":"monthNamesShort"))),o.setDate(e(this._get(t,a.match(/DD/)?"dayNames":"dayNamesShort"))+20-o.getDay())),t.input.attr("size",this._formatDate(t,o).length))},_inlineDatepicker:function(t,e){var i=V(t);i.hasClass(this.markerClassName)||(i.addClass(this.markerClassName).append(e.dpDiv),V.data(t,"datepicker",e),this._setDate(e,this._getDefaultDate(e),!0),this._updateDatepicker(e),this._updateAlternate(e),e.settings.disabled&&this._disableDatepicker(t),e.dpDiv.css("display","block"))},_dialogDatepicker:function(t,e,i,s,n){var o,a=this._dialogInst;return a||(this.uuid+=1,o="dp"+this.uuid,this._dialogInput=V("<input type='text' id='"+o+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.on("keydown",this._doKeyDown),V("body").append(this._dialogInput),(a=this._dialogInst=this._newInst(this._dialogInput,!1)).settings={},V.data(this._dialogInput[0],"datepicker",a)),at(a.settings,s||{}),e=e&&e.constructor===Date?this._formatDate(a,e):e,this._dialogInput.val(e),this._pos=n?n.length?n:[n.pageX,n.pageY]:null,this._pos||(o=document.documentElement.clientWidth,s=document.documentElement.clientHeight,e=document.documentElement.scrollLeft||document.body.scrollLeft,n=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[o/2-100+e,s/2-150+n]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),a.settings.onSelect=i,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),V.blockUI&&V.blockUI(this.dpDiv),V.data(this._dialogInput[0],"datepicker",a),this},_destroyDatepicker:function(t){var e,i=V(t),s=V.data(t,"datepicker");i.hasClass(this.markerClassName)&&(e=t.nodeName.toLowerCase(),V.removeData(t,"datepicker"),"input"===e?(s.append.remove(),s.trigger.remove(),i.removeClass(this.markerClassName).off("focus",this._showDatepicker).off("keydown",this._doKeyDown).off("keypress",this._doKeyPress).off("keyup",this._doKeyUp)):"div"!==e&&"span"!==e||i.removeClass(this.markerClassName).empty(),it===s&&(it=null,this._curInst=null))},_enableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!1,s.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().removeClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}))},_disableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!0,s.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().addClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}),this._disabledInputs[this._disabledInputs.length]=e)},_isDisabledDatepicker:function(t){if(!t)return!1;for(var e=0;e<this._disabledInputs.length;e++)if(this._disabledInputs[e]===t)return!0;return!1},_getInst:function(t){try{return V.data(t,"datepicker")}catch(t){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,e,i){var s,n,o=this._getInst(t);if(2===arguments.length&&"string"==typeof e)return"defaults"===e?V.extend({},V.datepicker._defaults):o?"all"===e?V.extend({},o.settings):this._get(o,e):null;s=e||{},"string"==typeof e&&((s={})[e]=i),o&&(this._curInst===o&&this._hideDatepicker(),n=this._getDateDatepicker(t,!0),e=this._getMinMaxDate(o,"min"),i=this._getMinMaxDate(o,"max"),at(o.settings,s),null!==e&&void 0!==s.dateFormat&&void 0===s.minDate&&(o.settings.minDate=this._formatDate(o,e)),null!==i&&void 0!==s.dateFormat&&void 0===s.maxDate&&(o.settings.maxDate=this._formatDate(o,i)),"disabled"in s&&(s.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(V(t),o),this._autoSize(o),this._setDate(o,n),this._updateAlternate(o),this._updateDatepicker(o))},_changeDatepicker:function(t,e,i){this._optionDatepicker(t,e,i)},_refreshDatepicker:function(t){t=this._getInst(t);t&&this._updateDatepicker(t)},_setDateDatepicker:function(t,e){t=this._getInst(t);t&&(this._setDate(t,e),this._updateDatepicker(t),this._updateAlternate(t))},_getDateDatepicker:function(t,e){t=this._getInst(t);return t&&!t.inline&&this._setDateFromField(t,e),t?this._getDate(t):null},_doKeyDown:function(t){var e,i,s=V.datepicker._getInst(t.target),n=!0,o=s.dpDiv.is(".ui-datepicker-rtl");if(s._keyEvent=!0,V.datepicker._datepickerShowing)switch(t.keyCode){case 9:V.datepicker._hideDatepicker(),n=!1;break;case 13:return(i=V("td."+V.datepicker._dayOverClass+":not(."+V.datepicker._currentClass+")",s.dpDiv))[0]&&V.datepicker._selectDay(t.target,s.selectedMonth,s.selectedYear,i[0]),(e=V.datepicker._get(s,"onSelect"))?(i=V.datepicker._formatDate(s),e.apply(s.input?s.input[0]:null,[i,s])):V.datepicker._hideDatepicker(),!1;case 27:V.datepicker._hideDatepicker();break;case 33:V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 34:V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&V.datepicker._clearDate(t.target),n=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&V.datepicker._gotoToday(t.target),n=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?1:-1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,-7,"D"),n=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?-1:1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,7,"D"),n=t.ctrlKey||t.metaKey;break;default:n=!1}else 36===t.keyCode&&t.ctrlKey?V.datepicker._showDatepicker(this):n=!1;n&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var e,i=V.datepicker._getInst(t.target);if(V.datepicker._get(i,"constrainInput"))return e=V.datepicker._possibleChars(V.datepicker._get(i,"dateFormat")),i=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||i<" "||!e||-1<e.indexOf(i)},_doKeyUp:function(t){t=V.datepicker._getInst(t.target);if(t.input.val()!==t.lastVal)try{V.datepicker.parseDate(V.datepicker._get(t,"dateFormat"),t.input?t.input.val():null,V.datepicker._getFormatConfig(t))&&(V.datepicker._setDateFromField(t),V.datepicker._updateAlternate(t),V.datepicker._updateDatepicker(t))}catch(t){}return!0},_showDatepicker:function(t){var e,i,s,n;"input"!==(t=t.target||t).nodeName.toLowerCase()&&(t=V("input",t.parentNode)[0]),V.datepicker._isDisabledDatepicker(t)||V.datepicker._lastInput===t||(n=V.datepicker._getInst(t),V.datepicker._curInst&&V.datepicker._curInst!==n&&(V.datepicker._curInst.dpDiv.stop(!0,!0),n&&V.datepicker._datepickerShowing&&V.datepicker._hideDatepicker(V.datepicker._curInst.input[0])),!1!==(i=(s=V.datepicker._get(n,"beforeShow"))?s.apply(t,[t,n]):{})&&(at(n.settings,i),n.lastVal=null,V.datepicker._lastInput=t,V.datepicker._setDateFromField(n),V.datepicker._inDialog&&(t.value=""),V.datepicker._pos||(V.datepicker._pos=V.datepicker._findPos(t),V.datepicker._pos[1]+=t.offsetHeight),e=!1,V(t).parents().each(function(){return!(e|="fixed"===V(this).css("position"))}),s={left:V.datepicker._pos[0],top:V.datepicker._pos[1]},V.datepicker._pos=null,n.dpDiv.empty(),n.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),V.datepicker._updateDatepicker(n),s=V.datepicker._checkOffset(n,s,e),n.dpDiv.css({position:V.datepicker._inDialog&&V.blockUI?"static":e?"fixed":"absolute",display:"none",left:s.left+"px",top:s.top+"px"}),n.inline||(i=V.datepicker._get(n,"showAnim"),s=V.datepicker._get(n,"duration"),n.dpDiv.css("z-index",function(t){for(var e,i;t.length&&t[0]!==document;){if(("absolute"===(e=t.css("position"))||"relative"===e||"fixed"===e)&&(i=parseInt(t.css("zIndex"),10),!isNaN(i)&&0!==i))return i;t=t.parent()}return 0}(V(t))+1),V.datepicker._datepickerShowing=!0,V.effects&&V.effects.effect[i]?n.dpDiv.show(i,V.datepicker._get(n,"showOptions"),s):n.dpDiv[i||"show"](i?s:null),V.datepicker._shouldFocusInput(n)&&n.input.trigger("focus"),V.datepicker._curInst=n)))},_updateDatepicker:function(t){this.maxRows=4,(it=t).dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var e,i=this._getNumberOfMonths(t),s=i[1],n=t.dpDiv.find("."+this._dayOverClass+" a"),o=V.datepicker._get(t,"onUpdateDatepicker");0<n.length&&ot.apply(n.get(0)),t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),1<s&&t.dpDiv.addClass("ui-datepicker-multi-"+s).css("width",17*s+"em"),t.dpDiv[(1!==i[0]||1!==i[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===V.datepicker._curInst&&V.datepicker._datepickerShowing&&V.datepicker._shouldFocusInput(t)&&t.input.trigger("focus"),t.yearshtml&&(e=t.yearshtml,setTimeout(function(){e===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year").first().replaceWith(t.yearshtml),e=t.yearshtml=null},0)),o&&o.apply(t.input?t.input[0]:null,[t])},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(t,e,i){var s=t.dpDiv.outerWidth(),n=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,a=t.input?t.input.outerHeight():0,r=document.documentElement.clientWidth+(i?0:V(document).scrollLeft()),l=document.documentElement.clientHeight+(i?0:V(document).scrollTop());return e.left-=this._get(t,"isRTL")?s-o:0,e.left-=i&&e.left===t.input.offset().left?V(document).scrollLeft():0,e.top-=i&&e.top===t.input.offset().top+a?V(document).scrollTop():0,e.left-=Math.min(e.left,e.left+s>r&&s<r?Math.abs(e.left+s-r):0),e.top-=Math.min(e.top,e.top+n>l&&n<l?Math.abs(n+a):0),e},_findPos:function(t){for(var e=this._getInst(t),i=this._get(e,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||V.expr.pseudos.hidden(t));)t=t[i?"previousSibling":"nextSibling"];return[(e=V(t).offset()).left,e.top]},_hideDatepicker:function(t){var e,i,s=this._curInst;!s||t&&s!==V.data(t,"datepicker")||this._datepickerShowing&&(e=this._get(s,"showAnim"),i=this._get(s,"duration"),t=function(){V.datepicker._tidyDialog(s)},V.effects&&(V.effects.effect[e]||V.effects[e])?s.dpDiv.hide(e,V.datepicker._get(s,"showOptions"),i,t):s.dpDiv["slideDown"===e?"slideUp":"fadeIn"===e?"fadeOut":"hide"](e?i:null,t),e||t(),this._datepickerShowing=!1,(t=this._get(s,"onClose"))&&t.apply(s.input?s.input[0]:null,[s.input?s.input.val():"",s]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),V.blockUI&&(V.unblockUI(),V("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).off(".ui-datepicker-calendar")},_checkExternalClick:function(t){var e;V.datepicker._curInst&&(e=V(t.target),t=V.datepicker._getInst(e[0]),(e[0].id===V.datepicker._mainDivId||0!==e.parents("#"+V.datepicker._mainDivId).length||e.hasClass(V.datepicker.markerClassName)||e.closest("."+V.datepicker._triggerClass).length||!V.datepicker._datepickerShowing||V.datepicker._inDialog&&V.blockUI)&&(!e.hasClass(V.datepicker.markerClassName)||V.datepicker._curInst===t)||V.datepicker._hideDatepicker())},_adjustDate:function(t,e,i){var s=V(t),t=this._getInst(s[0]);this._isDisabledDatepicker(s[0])||(this._adjustInstDate(t,e,i),this._updateDatepicker(t))},_gotoToday:function(t){var e=V(t),i=this._getInst(e[0]);this._get(i,"gotoCurrent")&&i.currentDay?(i.selectedDay=i.currentDay,i.drawMonth=i.selectedMonth=i.currentMonth,i.drawYear=i.selectedYear=i.currentYear):(t=new Date,i.selectedDay=t.getDate(),i.drawMonth=i.selectedMonth=t.getMonth(),i.drawYear=i.selectedYear=t.getFullYear()),this._notifyChange(i),this._adjustDate(e)},_selectMonthYear:function(t,e,i){var s=V(t),t=this._getInst(s[0]);t["selected"+("M"===i?"Month":"Year")]=t["draw"+("M"===i?"Month":"Year")]=parseInt(e.options[e.selectedIndex].value,10),this._notifyChange(t),this._adjustDate(s)},_selectDay:function(t,e,i,s){var n=V(t);V(s).hasClass(this._unselectableClass)||this._isDisabledDatepicker(n[0])||((n=this._getInst(n[0])).selectedDay=n.currentDay=parseInt(V("a",s).attr("data-date")),n.selectedMonth=n.currentMonth=e,n.selectedYear=n.currentYear=i,this._selectDate(t,this._formatDate(n,n.currentDay,n.currentMonth,n.currentYear)))},_clearDate:function(t){t=V(t);this._selectDate(t,"")},_selectDate:function(t,e){var i=V(t),t=this._getInst(i[0]);e=null!=e?e:this._formatDate(t),t.input&&t.input.val(e),this._updateAlternate(t),(i=this._get(t,"onSelect"))?i.apply(t.input?t.input[0]:null,[e,t]):t.input&&t.input.trigger("change"),t.inline?this._updateDatepicker(t):(this._hideDatepicker(),this._lastInput=t.input[0],"object"!=typeof t.input[0]&&t.input.trigger("focus"),this._lastInput=null)},_updateAlternate:function(t){var e,i,s=this._get(t,"altField");s&&(e=this._get(t,"altFormat")||this._get(t,"dateFormat"),i=this._getDate(t),t=this.formatDate(e,i,this._getFormatConfig(t)),V(document).find(s).val(t))},noWeekends:function(t){t=t.getDay();return[0<t&&t<6,""]},iso8601Week:function(t){var e=new Date(t.getTime());return e.setDate(e.getDate()+4-(e.getDay()||7)),t=e.getTime(),e.setMonth(0),e.setDate(1),Math.floor(Math.round((t-e)/864e5)/7)+1},parseDate:function(e,n,t){if(null==e||null==n)throw"Invalid arguments";if(""===(n="object"==typeof n?n.toString():n+""))return null;for(var i,s,o,a=0,r=(t?t.shortYearCutoff:null)||this._defaults.shortYearCutoff,r="string"!=typeof r?r:(new Date).getFullYear()%100+parseInt(r,10),l=(t?t.dayNamesShort:null)||this._defaults.dayNamesShort,h=(t?t.dayNames:null)||this._defaults.dayNames,c=(t?t.monthNamesShort:null)||this._defaults.monthNamesShort,u=(t?t.monthNames:null)||this._defaults.monthNames,d=-1,p=-1,f=-1,g=-1,m=!1,_=function(t){t=w+1<e.length&&e.charAt(w+1)===t;return t&&w++,t},v=function(t){var e=_(t),e="@"===t?14:"!"===t?20:"y"===t&&e?4:"o"===t?3:2,e=new RegExp("^\\d{"+("y"===t?e:1)+","+e+"}"),e=n.substring(a).match(e);if(!e)throw"Missing number at position "+a;return a+=e[0].length,parseInt(e[0],10)},b=function(t,e,i){var s=-1,e=V.map(_(t)?i:e,function(t,e){return[[e,t]]}).sort(function(t,e){return-(t[1].length-e[1].length)});if(V.each(e,function(t,e){var i=e[1];if(n.substr(a,i.length).toLowerCase()===i.toLowerCase())return s=e[0],a+=i.length,!1}),-1!==s)return s+1;throw"Unknown name at position "+a},y=function(){if(n.charAt(a)!==e.charAt(w))throw"Unexpected literal at position 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V.datepicker._selectMonthYear(i,this,"M"),!1},selectYear:function(){return V.datepicker._selectMonthYear(i,this,"Y"),!1}};V(this).on(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(t){var e,i,s,n,o,a,r,l,h,c,u,d,p,f,g,m,_,v,b,y,w,x,k,C,D,I,T,P,M,S,H,z,A=new Date,O=this._daylightSavingAdjust(new Date(A.getFullYear(),A.getMonth(),A.getDate())),N=this._get(t,"isRTL"),E=this._get(t,"showButtonPanel"),W=this._get(t,"hideIfNoPrevNext"),F=this._get(t,"navigationAsDateFormat"),L=this._getNumberOfMonths(t),R=this._get(t,"showCurrentAtPos"),A=this._get(t,"stepMonths"),Y=1!==L[0]||1!==L[1],B=this._daylightSavingAdjust(t.currentDay?new Date(t.currentYear,t.currentMonth,t.currentDay):new Date(9999,9,9)),j=this._getMinMaxDate(t,"min"),q=this._getMinMaxDate(t,"max"),K=t.drawMonth-R,U=t.drawYear;if(K<0&&(K+=12,U--),q)for(e=this._daylightSavingAdjust(new 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ui-helper-clearfix"+y+"'>"+(/all|left/.test(y)&&0===m?N?s:i:"")+(/all|right/.test(y)&&0===m?N?i:s:"")+this._generateMonthYearHeader(t,K,U,j,q,0<m||0<v,l,h)+"</div><table class='ui-datepicker-calendar'><thead><tr>",x=o?"<th class='ui-datepicker-week-col'>"+this._get(t,"weekHeader")+"</th>":"",g=0;g<7;g++)x+="<th scope='col'"+(5<=(g+n+6)%7?" class='ui-datepicker-week-end'":"")+"><span title='"+a[k=(g+n)%7]+"'>"+r[k]+"</span></th>";for(w+=x+"</tr></thead><tbody>",D=this._getDaysInMonth(U,K),U===t.selectedYear&&K===t.selectedMonth&&(t.selectedDay=Math.min(t.selectedDay,D)),C=(this._getFirstDayOfMonth(U,K)-n+7)%7,D=Math.ceil((C+D)/7),I=Y&&this.maxRows>D?this.maxRows:D,this.maxRows=I,T=this._daylightSavingAdjust(new Date(U,K,1-C)),P=0;P<I;P++){for(w+="<tr>",M=o?"<td class='ui-datepicker-week-col'>"+this._get(t,"calculateWeek")(T)+"</td>":"",g=0;g<7;g++)S=c?c.apply(t.input?t.input[0]:null,[T]):[!0,""],z=(H=T.getMonth()!==K)&&!d||!S[0]||j&&T<j||q&&q<T,M+="<td class='"+(5<=(g+n+6)%7?" ui-datepicker-week-end":"")+(H?" ui-datepicker-other-month":"")+(T.getTime()===b.getTime()&&K===t.selectedMonth&&t._keyEvent||p.getTime()===T.getTime()&&p.getTime()===b.getTime()?" 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l,h,c,u,d,p,f=this._get(t,"changeMonth"),g=this._get(t,"changeYear"),m=this._get(t,"showMonthAfterYear"),_=this._get(t,"selectMonthLabel"),v=this._get(t,"selectYearLabel"),b="<div class='ui-datepicker-title'>",y="";if(o||!f)y+="<span class='ui-datepicker-month'>"+a[e]+"</span>";else{for(l=s&&s.getFullYear()===i,h=n&&n.getFullYear()===i,y+="<select class='ui-datepicker-month' aria-label='"+_+"' data-handler='selectMonth' data-event='change'>",c=0;c<12;c++)(!l||c>=s.getMonth())&&(!h||c<=n.getMonth())&&(y+="<option value='"+c+"'"+(c===e?" selected='selected'":"")+">"+r[c]+"</option>");y+="</select>"}if(m||(b+=y+(!o&&f&&g?"":"&#xa0;")),!t.yearshtml)if(t.yearshtml="",o||!g)b+="<span class='ui-datepicker-year'>"+i+"</span>";else{for(a=this._get(t,"yearRange").split(":"),u=(new Date).getFullYear(),d=(_=function(t){t=t.match(/c[+\-].*/)?i+parseInt(t.substring(1),10):t.match(/[+\-].*/)?u+parseInt(t,10):parseInt(t,10);return 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t&&t<e?t:e},_notifyChange:function(t){var e=this._get(t,"onChangeMonthYear");e&&e.apply(t.input?t.input[0]:null,[t.selectedYear,t.selectedMonth+1,t])},_getNumberOfMonths:function(t){t=this._get(t,"numberOfMonths");return null==t?[1,1]:"number"==typeof t?[1,t]:t},_getMinMaxDate:function(t,e){return this._determineDate(t,this._get(t,e+"Date"),null)},_getDaysInMonth:function(t,e){return 32-this._daylightSavingAdjust(new Date(t,e,32)).getDate()},_getFirstDayOfMonth:function(t,e){return new Date(t,e,1).getDay()},_canAdjustMonth:function(t,e,i,s){var n=this._getNumberOfMonths(t),n=this._daylightSavingAdjust(new Date(i,s+(e<0?e:n[0]*n[1]),1));return e<0&&n.setDate(this._getDaysInMonth(n.getFullYear(),n.getMonth())),this._isInRange(t,n)},_isInRange:function(t,e){var i=this._getMinMaxDate(t,"min"),s=this._getMinMaxDate(t,"max"),n=null,o=null,a=this._get(t,"yearRange");return a&&(t=a.split(":"),a=(new 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this;V.datepicker.initialized||(V(document).on("mousedown",V.datepicker._checkExternalClick),V.datepicker.initialized=!0),0===V("#"+V.datepicker._mainDivId).length&&V("body").append(V.datepicker.dpDiv);var e=Array.prototype.slice.call(arguments,1);return"string"==typeof t&&("isDisabled"===t||"getDate"===t||"widget"===t)||"option"===t&&2===arguments.length&&"string"==typeof arguments[1]?V.datepicker["_"+t+"Datepicker"].apply(V.datepicker,[this[0]].concat(e)):this.each(function(){"string"==typeof t?V.datepicker["_"+t+"Datepicker"].apply(V.datepicker,[this].concat(e)):V.datepicker._attachDatepicker(this,t)})},V.datepicker=new st,V.datepicker.initialized=!1,V.datepicker.uuid=(new Date).getTime(),V.datepicker.version="1.13.2";V.datepicker,V.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase());var rt=!1;V(document).on("mouseup",function(){rt=!1});V.widget("ui.mouse",{version:"1.13.2",options:{cancel:"input, textarea, button, select, 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i&&!s&&this._mouseCapture(t)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(t),!this._mouseStarted)?(t.preventDefault(),!0):(!0===V.data(t.target,this.widgetName+".preventClickEvent")&&V.removeData(t.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(t){return e._mouseMove(t)},this._mouseUpDelegate=function(t){return e._mouseUp(t)},this.document.on("mousemove."+this.widgetName,this._mouseMoveDelegate).on("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),rt=!0)):!0}},_mouseMove:function(t){if(this._mouseMoved){if(V.ui.ie&&(!document.documentMode||document.documentMode<9)&&!t.button)return this._mouseUp(t);if(!t.which)if(t.originalEvent.altKey||t.originalEvent.ctrlKey||t.originalEvent.metaKey||t.originalEvent.shiftKey)this.ignoreMissingWhich=!0;else if(!this.ignoreMissingWhich)return this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(this._mouseDownEvent,t),this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&V.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),this._mouseDelayTimer&&(clearTimeout(this._mouseDelayTimer),delete this._mouseDelayTimer),this.ignoreMissingWhich=!1,rt=!1,t.preventDefault()},_mouseDistanceMet:function(t){return Math.max(Math.abs(this._mouseDownEvent.pageX-t.pageX),Math.abs(this._mouseDownEvent.pageY-t.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),V.ui.plugin={add:function(t,e,i){var s,n=V.ui[t].prototype;for(s in i)n.plugins[s]=n.plugins[s]||[],n.plugins[s].push([e,i[s]])},call:function(t,e,i,s){var n,o=t.plugins[e];if(o&&(s||t.element[0].parentNode&&11!==t.element[0].parentNode.nodeType))for(n=0;n<o.length;n++)t.options[o[n][0]]&&o[n][1].apply(t.element,i)}},V.ui.safeBlur=function(t){t&&"body"!==t.nodeName.toLowerCase()&&V(t).trigger("blur")};V.widget("ui.draggable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this._addClass("ui-draggable"),this._setHandleClassName(),this._mouseInit()},_setOption:function(t,e){this._super(t,e),"handle"===t&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){(this.helper||this.element).is(".ui-draggable-dragging")?this.destroyOnClear=!0:(this._removeHandleClassName(),this._mouseDestroy())},_mouseCapture:function(t){var e=this.options;return!(this.helper||e.disabled||0<V(t.target).closest(".ui-resizable-handle").length)&&(this.handle=this._getHandle(t),!!this.handle&&(this._blurActiveElement(t),this._blockFrames(!0===e.iframeFix?"iframe":e.iframeFix),!0))},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=V(this);return V("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var e=V.ui.safeActiveElement(this.document[0]);V(t.target).closest(e).length||V.ui.safeBlur(e)},_mouseStart:function(t){var e=this.options;return this.helper=this._createHelper(t),this._addClass(this.helper,"ui-draggable-dragging"),this._cacheHelperProportions(),V.ui.ddmanager&&(V.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=0<this.helper.parents().filter(function(){return"fixed"===V(this).css("position")}).length,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,e.cursorAt&&this._adjustOffsetFromHelper(e.cursorAt),this._setContainment(),!1===this._trigger("start",t)?(this._clear(),!1):(this._cacheHelperProportions(),V.ui.ddmanager&&!e.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this._mouseDrag(t,!0),V.ui.ddmanager&&V.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(t){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:t.pageX-this.offset.left,top:t.pageY-this.offset.top}},_mouseDrag:function(t,e){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!e){e=this._uiHash();if(!1===this._trigger("drag",t,e))return 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this.helper.is(".ui-draggable-dragging")?this._mouseUp(new V.Event("mouseup",{target:this.element[0]})):this._clear(),this},_getHandle:function(t){return!this.options.handle||!!V(t.target).closest(this.element.find(this.options.handle)).length},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this._addClass(this.handleElement,"ui-draggable-handle")},_removeHandleClassName:function(){this._removeClass(this.handleElement,"ui-draggable-handle")},_createHelper:function(t){var e=this.options,i="function"==typeof e.helper,t=i?V(e.helper.apply(this.element[0],[t])):"clone"===e.helper?this.element.clone().removeAttr("id"):this.element;return t.parents("body").length||t.appendTo("parent"===e.appendTo?this.element[0].parentNode:e.appendTo),i&&t[0]===this.element[0]&&this._setPositionRelative(),t[0]===this.element[0]||/(fixed|absolute)/.test(t.css("position"))||t.css("position","absolute"),t},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(t){return/(html|body)/i.test(t.tagName)||t===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),e=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==e&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this._isRootNode(this.offsetParent[0])?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.element.position(),e=this._isRootNode(this.scrollParent[0]);return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+(e?0:this.scrollParent.scrollTop()),left:t.left-(parseInt(this.helper.css("left"),10)||0)+(e?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i,s=this.options,n=this.document[0];this.relativeContainer=null,s.containment?"window"!==s.containment?"document"!==s.containment?s.containment.constructor!==Array?("parent"===s.containment&&(s.containment=this.helper[0].parentNode),(i=(e=V(s.containment))[0])&&(t=/(scroll|auto)/.test(e.css("overflow")),this.containment=[(parseInt(e.css("borderLeftWidth"),10)||0)+(parseInt(e.css("paddingLeft"),10)||0),(parseInt(e.css("borderTopWidth"),10)||0)+(parseInt(e.css("paddingTop"),10)||0),(t?Math.max(i.scrollWidth,i.offsetWidth):i.offsetWidth)-(parseInt(e.css("borderRightWidth"),10)||0)-(parseInt(e.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(i.scrollHeight,i.offsetHeight):i.offsetHeight)-(parseInt(e.css("borderBottomWidth"),10)||0)-(parseInt(e.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=e)):this.containment=s.containment:this.containment=[0,0,V(n).width()-this.helperProportions.width-this.margins.left,(V(n).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=[V(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,V(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,V(window).scrollLeft()+V(window).width()-this.helperProportions.width-this.margins.left,V(window).scrollTop()+(V(window).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=null},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,t=this._isRootNode(this.scrollParent[0]);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:t?0:this.offset.scroll.top)*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:t?0:this.offset.scroll.left)*i}},_generatePosition:function(t,e){var i,s=this.options,n=this._isRootNode(this.scrollParent[0]),o=t.pageX,a=t.pageY;return 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i=i||this._uiHash(),V.ui.plugin.call(this,t,[e,i,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),i.offset=this.positionAbs),V.Widget.prototype._trigger.call(this,t,e,i)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),V.ui.plugin.add("draggable","connectToSortable",{start:function(e,t,i){var s=V.extend({},t,{item:i.element});i.sortables=[],V(i.options.connectToSortable).each(function(){var t=V(this).sortable("instance");t&&!t.options.disabled&&(i.sortables.push(t),t.refreshPositions(),t._trigger("activate",e,s))})},stop:function(e,t,i){var s=V.extend({},t,{item:i.element});i.cancelHelperRemoval=!1,V.each(i.sortables,function(){var t=this;t.isOver?(t.isOver=0,i.cancelHelperRemoval=!0,t.cancelHelperRemoval=!1,t._storedCSS={position:t.placeholder.css("position"),top:t.placeholder.css("top"),left:t.placeholder.css("left")},t._mouseStop(e),t.options.helper=t.options._helper):(t.cancelHelperRemoval=!0,t._trigger("deactivate",e,s))})},drag:function(i,s,n){V.each(n.sortables,function(){var t=!1,e=this;e.positionAbs=n.positionAbs,e.helperProportions=n.helperProportions,e.offset.click=n.offset.click,e._intersectsWith(e.containerCache)&&(t=!0,V.each(n.sortables,function(){return this.positionAbs=n.positionAbs,this.helperProportions=n.helperProportions,this.offset.click=n.offset.click,t=this!==e&&this._intersectsWith(this.containerCache)&&V.contains(e.element[0],this.element[0])?!1:t})),t?(e.isOver||(e.isOver=1,n._parent=s.helper.parent(),e.currentItem=s.helper.appendTo(e.element).data("ui-sortable-item",!0),e.options._helper=e.options.helper,e.options.helper=function(){return s.helper[0]},i.target=e.currentItem[0],e._mouseCapture(i,!0),e._mouseStart(i,!0,!0),e.offset.click.top=n.offset.click.top,e.offset.click.left=n.offset.click.left,e.offset.parent.left-=n.offset.parent.left-e.offset.parent.left,e.offset.parent.top-=n.offset.parent.top-e.offset.parent.top,n._trigger("toSortable",i),n.dropped=e.element,V.each(n.sortables,function(){this.refreshPositions()}),n.currentItem=n.element,e.fromOutside=n),e.currentItem&&(e._mouseDrag(i),s.position=e.position)):e.isOver&&(e.isOver=0,e.cancelHelperRemoval=!0,e.options._revert=e.options.revert,e.options.revert=!1,e._trigger("out",i,e._uiHash(e)),e._mouseStop(i,!0),e.options.revert=e.options._revert,e.options.helper=e.options._helper,e.placeholder&&e.placeholder.remove(),s.helper.appendTo(n._parent),n._refreshOffsets(i),s.position=n._generatePosition(i,!0),n._trigger("fromSortable",i),n.dropped=!1,V.each(n.sortables,function(){this.refreshPositions()}))})}}),V.ui.plugin.add("draggable","cursor",{start:function(t,e,i){var s=V("body"),i=i.options;s.css("cursor")&&(i._cursor=s.css("cursor")),s.css("cursor",i.cursor)},stop:function(t,e,i){i=i.options;i._cursor&&V("body").css("cursor",i._cursor)}}),V.ui.plugin.add("draggable","opacity",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("opacity")&&(i._opacity=e.css("opacity")),e.css("opacity",i.opacity)},stop:function(t,e,i){i=i.options;i._opacity&&V(e.helper).css("opacity",i._opacity)}}),V.ui.plugin.add("draggable","scroll",{start:function(t,e,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,e,i){var s=i.options,n=!1,o=i.scrollParentNotHidden[0],a=i.document[0];o!==a&&"HTML"!==o.tagName?(s.axis&&"x"===s.axis||(i.overflowOffset.top+o.offsetHeight-t.pageY<s.scrollSensitivity?o.scrollTop=n=o.scrollTop+s.scrollSpeed:t.pageY-i.overflowOffset.top<s.scrollSensitivity&&(o.scrollTop=n=o.scrollTop-s.scrollSpeed)),s.axis&&"y"===s.axis||(i.overflowOffset.left+o.offsetWidth-t.pageX<s.scrollSensitivity?o.scrollLeft=n=o.scrollLeft+s.scrollSpeed:t.pageX-i.overflowOffset.left<s.scrollSensitivity&&(o.scrollLeft=n=o.scrollLeft-s.scrollSpeed))):(s.axis&&"x"===s.axis||(t.pageY-V(a).scrollTop()<s.scrollSensitivity?n=V(a).scrollTop(V(a).scrollTop()-s.scrollSpeed):V(window).height()-(t.pageY-V(a).scrollTop())<s.scrollSensitivity&&(n=V(a).scrollTop(V(a).scrollTop()+s.scrollSpeed))),s.axis&&"y"===s.axis||(t.pageX-V(a).scrollLeft()<s.scrollSensitivity?n=V(a).scrollLeft(V(a).scrollLeft()-s.scrollSpeed):V(window).width()-(t.pageX-V(a).scrollLeft())<s.scrollSensitivity&&(n=V(a).scrollLeft(V(a).scrollLeft()+s.scrollSpeed)))),!1!==n&&V.ui.ddmanager&&!s.dropBehaviour&&V.ui.ddmanager.prepareOffsets(i,t)}}),V.ui.plugin.add("draggable","snap",{start:function(t,e,i){var s=i.options;i.snapElements=[],V(s.snap.constructor!==String?s.snap.items||":data(ui-draggable)":s.snap).each(function(){var t=V(this),e=t.offset();this!==i.element[0]&&i.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:e.top,left:e.left})})},drag:function(t,e,i){for(var 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s,i=i.options,i=V.makeArray(V(i.stack)).sort(function(t,e){return(parseInt(V(t).css("zIndex"),10)||0)-(parseInt(V(e).css("zIndex"),10)||0)});i.length&&(s=parseInt(V(i[0]).css("zIndex"),10)||0,V(i).each(function(t){V(this).css("zIndex",s+t)}),this.css("zIndex",s+i.length))}}),V.ui.plugin.add("draggable","zIndex",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("zIndex")&&(i._zIndex=e.css("zIndex")),e.css("zIndex",i.zIndex)},stop:function(t,e,i){i=i.options;i._zIndex&&V(e.helper).css("zIndex",i._zIndex)}});V.ui.draggable;V.widget("ui.resizable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,classes:{"ui-resizable-se":"ui-icon ui-icon-gripsmall-diagonal-se"},containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(t){return parseFloat(t)||0},_isNumber:function(t){return!isNaN(parseFloat(t))},_hasScroll:function(t,e){if("hidden"===V(t).css("overflow"))return!1;var i=e&&"left"===e?"scrollLeft":"scrollTop",e=!1;if(0<t[i])return!0;try{t[i]=1,e=0<t[i],t[i]=0}catch(t){}return e},_create:function(){var t,e=this.options,i=this;this._addClass("ui-resizable"),V.extend(this,{_aspectRatio:!!e.aspectRatio,aspectRatio:e.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:e.helper||e.ghost||e.animate?e.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(V("<div 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t(t){V(t).removeData("resizable").removeData("ui-resizable").off(".resizable")}var e;return this.elementIsWrapper&&(t(this.element),e=this.element,this.originalElement.css({position:e.css("position"),width:e.outerWidth(),height:e.outerHeight(),top:e.css("top"),left:e.css("left")}).insertAfter(e),e.remove()),this.originalElement.css("resize",this.originalResizeStyle),t(this.originalElement),this},_setOption:function(t,e){switch(this._super(t,e),t){case"handles":this._removeHandles(),this._setupHandles();break;case"aspectRatio":this._aspectRatio=!!e}},_setupHandles:function(){var t,e,i,s,n,o=this.options,a=this;if(this.handles=o.handles||(V(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=V(),this._addedHandles=V(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),i=this.handles.split(","),this.handles={},e=0;e<i.length;e++)s="ui-resizable-"+(t=String.prototype.trim.call(i[e])),n=V("<div>"),this._addClass(n,"ui-resizable-handle "+s),n.css({zIndex:o.zIndex}),this.handles[t]=".ui-resizable-"+t,this.element.children(this.handles[t]).length||(this.element.append(n),this._addedHandles=this._addedHandles.add(n));this._renderAxis=function(t){var e,i,s;for(e in 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s=this.originalSize;return{top:this.originalPosition.top+i,height:s.height-i}},s:function(t,e,i){return{height:this.originalSize.height+i}},se:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},sw:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,e,i]))},ne:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},nw:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,e,i]))}},_propagate:function(t,e){V.ui.plugin.call(this,t,[e,this.ui()]),"resize"!==t&&this._trigger(t,e,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),V.ui.plugin.add("resizable","animate",{stop:function(e){var i=V(this).resizable("instance"),t=i.options,s=i._proportionallyResizeElements,n=s.length&&/textarea/i.test(s[0].nodeName),o=n&&i._hasScroll(s[0],"left")?0:i.sizeDiff.height,a=n?0:i.sizeDiff.width,n={width:i.size.width-a,height:i.size.height-o},a=parseFloat(i.element.css("left"))+(i.position.left-i.originalPosition.left)||null,o=parseFloat(i.element.css("top"))+(i.position.top-i.originalPosition.top)||null;i.element.animate(V.extend(n,o&&a?{top:o,left:a}:{}),{duration:t.animateDuration,easing:t.animateEasing,step:function(){var t={width:parseFloat(i.element.css("width")),height:parseFloat(i.element.css("height")),top:parseFloat(i.element.css("top")),left:parseFloat(i.element.css("left"))};s&&s.length&&V(s[0]).css({width:t.width,height:t.height}),i._updateCache(t),i._propagate("resize",e)}})}}),V.ui.plugin.add("resizable","containment",{start:function(){var i,s,n=V(this).resizable("instance"),t=n.options,e=n.element,o=t.containment,a=o instanceof V?o.get(0):/parent/.test(o)?e.parent().get(0):o;a&&(n.containerElement=V(a),/document/.test(o)||o===document?(n.containerOffset={left:0,top:0},n.containerPosition={left:0,top:0},n.parentData={element:V(document),left:0,top:0,width:V(document).width(),height:V(document).height()||document.body.parentNode.scrollHeight}):(i=V(a),s=[],V(["Top","Right","Left","Bottom"]).each(function(t,e){s[t]=n._num(i.css("padding"+e))}),n.containerOffset=i.offset(),n.containerPosition=i.position(),n.containerSize={height:i.innerHeight()-s[3],width:i.innerWidth()-s[1]},t=n.containerOffset,e=n.containerSize.height,o=n.containerSize.width,o=n._hasScroll(a,"left")?a.scrollWidth:o,e=n._hasScroll(a)?a.scrollHeight:e,n.parentData={element:a,left:t.left,top:t.top,width:o,height:e}))},resize:function(t){var e=V(this).resizable("instance"),i=e.options,s=e.containerOffset,n=e.position,o=e._aspectRatio||t.shiftKey,a={top:0,left:0},r=e.containerElement,t=!0;r[0]!==document&&/static/.test(r.css("position"))&&(a=s),n.left<(e._helper?s.left:0)&&(e.size.width=e.size.width+(e._helper?e.position.left-s.left:e.position.left-a.left),o&&(e.size.height=e.size.width/e.aspectRatio,t=!1),e.position.left=i.helper?s.left:0),n.top<(e._helper?s.top:0)&&(e.size.height=e.size.height+(e._helper?e.position.top-s.top:e.position.top),o&&(e.size.width=e.size.height*e.aspectRatio,t=!1),e.position.top=e._helper?s.top:0),i=e.containerElement.get(0)===e.element.parent().get(0),n=/relative|absolute/.test(e.containerElement.css("position")),i&&n?(e.offset.left=e.parentData.left+e.position.left,e.offset.top=e.parentData.top+e.position.top):(e.offset.left=e.element.offset().left,e.offset.top=e.element.offset().top),n=Math.abs(e.sizeDiff.width+(e._helper?e.offset.left-a.left:e.offset.left-s.left)),s=Math.abs(e.sizeDiff.height+(e._helper?e.offset.top-a.top:e.offset.top-s.top)),n+e.size.width>=e.parentData.width&&(e.size.width=e.parentData.width-n,o&&(e.size.height=e.size.width/e.aspectRatio,t=!1)),s+e.size.height>=e.parentData.height&&(e.size.height=e.parentData.height-s,o&&(e.size.width=e.size.height*e.aspectRatio,t=!1)),t||(e.position.left=e.prevPosition.left,e.position.top=e.prevPosition.top,e.size.width=e.prevSize.width,e.size.height=e.prevSize.height)},stop:function(){var t=V(this).resizable("instance"),e=t.options,i=t.containerOffset,s=t.containerPosition,n=t.containerElement,o=V(t.helper),a=o.offset(),r=o.outerWidth()-t.sizeDiff.width,o=o.outerHeight()-t.sizeDiff.height;t._helper&&!e.animate&&/relative/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o}),t._helper&&!e.animate&&/static/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o})}}),V.ui.plugin.add("resizable","alsoResize",{start:function(){var t=V(this).resizable("instance").options;V(t.alsoResize).each(function(){var t=V(this);t.data("ui-resizable-alsoresize",{width:parseFloat(t.width()),height:parseFloat(t.height()),left:parseFloat(t.css("left")),top:parseFloat(t.css("top"))})})},resize:function(t,i){var e=V(this).resizable("instance"),s=e.options,n=e.originalSize,o=e.originalPosition,a={height:e.size.height-n.height||0,width:e.size.width-n.width||0,top:e.position.top-o.top||0,left:e.position.left-o.left||0};V(s.alsoResize).each(function(){var t=V(this),s=V(this).data("ui-resizable-alsoresize"),n={},e=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];V.each(e,function(t,e){var i=(s[e]||0)+(a[e]||0);i&&0<=i&&(n[e]=i||null)}),t.css(n)})},stop:function(){V(this).removeData("ui-resizable-alsoresize")}}),V.ui.plugin.add("resizable","ghost",{start:function(){var t=V(this).resizable("instance"),e=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:e.height,width:e.width,margin:0,left:0,top:0}),t._addClass(t.ghost,"ui-resizable-ghost"),!1!==V.uiBackCompat&&"string"==typeof t.options.ghost&&t.ghost.addClass(this.options.ghost),t.ghost.appendTo(t.helper)},resize:function(){var t=V(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=V(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),V.ui.plugin.add("resizable","grid",{resize:function(){var t,e=V(this).resizable("instance"),i=e.options,s=e.size,n=e.originalSize,o=e.originalPosition,a=e.axis,r="number"==typeof i.grid?[i.grid,i.grid]:i.grid,l=r[0]||1,h=r[1]||1,c=Math.round((s.width-n.width)/l)*l,u=Math.round((s.height-n.height)/h)*h,d=n.width+c,p=n.height+u,f=i.maxWidth&&i.maxWidth<d,g=i.maxHeight&&i.maxHeight<p,m=i.minWidth&&i.minWidth>d,s=i.minHeight&&i.minHeight>p;i.grid=r,m&&(d+=l),s&&(p+=h),f&&(d-=l),g&&(p-=h),/^(se|s|e)$/.test(a)?(e.size.width=d,e.size.height=p):/^(ne)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.top=o.top-u):/^(sw)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.left=o.left-c):((p-h<=0||d-l<=0)&&(t=e._getPaddingPlusBorderDimensions(this)),0<p-h?(e.size.height=p,e.position.top=o.top-u):(p=h-t.height,e.size.height=p,e.position.top=o.top+n.height-p),0<d-l?(e.size.width=d,e.position.left=o.left-c):(d=l-t.width,e.size.width=d,e.position.left=o.left+n.width-d))}});V.ui.resizable;V.widget("ui.dialog",{version:"1.13.2",options:{appendTo:"body",autoOpen:!0,buttons:[],classes:{"ui-dialog":"ui-corner-all","ui-dialog-titlebar":"ui-corner-all"},closeOnEscape:!0,closeText:"Close",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var e=V(this).css(t).offset().top;e<0&&V(this).css("top",t.top-e)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),null==this.options.title&&null!=this.originalTitle&&(this.options.title=this.originalTitle),this.options.disabled&&(this.options.disabled=!1),this._createWrapper(),this.element.show().removeAttr("title").appendTo(this.uiDialog),this._addClass("ui-dialog-content","ui-widget-content"),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&V.fn.draggable&&this._makeDraggable(),this.options.resizable&&V.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?V(t):this.document.find(t||"body").eq(0)},_destroy:function(){var t,e=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().css(this.originalCss).detach(),this.uiDialog.remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),(t=e.parent.children().eq(e.index)).length&&t[0]!==this.element[0]?t.before(this.element):e.parent.append(this.element)},widget:function(){return this.uiDialog},disable:V.noop,enable:V.noop,close:function(t){var e=this;this._isOpen&&!1!==this._trigger("beforeClose",t)&&(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),this.opener.filter(":focusable").trigger("focus").length||V.ui.safeBlur(V.ui.safeActiveElement(this.document[0])),this._hide(this.uiDialog,this.options.hide,function(){e._trigger("close",t)}))},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,e){var i=!1,s=this.uiDialog.siblings(".ui-front:visible").map(function(){return+V(this).css("z-index")}).get(),s=Math.max.apply(null,s);return s>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",s+1),i=!0),i&&!e&&this._trigger("focus",t),i},open:function(){var t=this;this._isOpen?this._moveToTop()&&this._focusTabbable():(this._isOpen=!0,this.opener=V(V.ui.safeActiveElement(this.document[0])),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"))},_focusTabbable:function(){var t=this._focusedElement;(t=!(t=!(t=!(t=!(t=t||this.element.find("[autofocus]")).length?this.element.find(":tabbable"):t).length?this.uiDialogButtonPane.find(":tabbable"):t).length?this.uiDialogTitlebarClose.filter(":tabbable"):t).length?this.uiDialog:t).eq(0).trigger("focus")},_restoreTabbableFocus:function(){var t=V.ui.safeActiveElement(this.document[0]);this.uiDialog[0]===t||V.contains(this.uiDialog[0],t)||this._focusTabbable()},_keepFocus:function(t){t.preventDefault(),this._restoreTabbableFocus(),this._delay(this._restoreTabbableFocus)},_createWrapper:function(){this.uiDialog=V("<div>").hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._addClass(this.uiDialog,"ui-dialog","ui-widget ui-widget-content ui-front"),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===V.ui.keyCode.ESCAPE)return t.preventDefault(),void this.close(t);var e,i,s;t.keyCode!==V.ui.keyCode.TAB||t.isDefaultPrevented()||(e=this.uiDialog.find(":tabbable"),i=e.first(),s=e.last(),t.target!==s[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==i[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){s.trigger("focus")}),t.preventDefault()):(this._delay(function(){i.trigger("focus")}),t.preventDefault()))},mousedown:function(t){this._moveToTop(t)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=V("<div>"),this._addClass(this.uiDialogTitlebar,"ui-dialog-titlebar","ui-widget-header ui-helper-clearfix"),this._on(this.uiDialogTitlebar,{mousedown:function(t){V(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.trigger("focus")}}),this.uiDialogTitlebarClose=V("<button type='button'></button>").button({label:V("<a>").text(this.options.closeText).html(),icon:"ui-icon-closethick",showLabel:!1}).appendTo(this.uiDialogTitlebar),this._addClass(this.uiDialogTitlebarClose,"ui-dialog-titlebar-close"),this._on(this.uiDialogTitlebarClose,{click:function(t){t.preventDefault(),this.close(t)}}),t=V("<span>").uniqueId().prependTo(this.uiDialogTitlebar),this._addClass(t,"ui-dialog-title"),this._title(t),this.uiDialogTitlebar.prependTo(this.uiDialog),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(t){this.options.title?t.text(this.options.title):t.html("&#160;")},_createButtonPane:function(){this.uiDialogButtonPane=V("<div>"),this._addClass(this.uiDialogButtonPane,"ui-dialog-buttonpane","ui-widget-content ui-helper-clearfix"),this.uiButtonSet=V("<div>").appendTo(this.uiDialogButtonPane),this._addClass(this.uiButtonSet,"ui-dialog-buttonset"),this._createButtons()},_createButtons:function(){var s=this,t=this.options.buttons;this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),V.isEmptyObject(t)||Array.isArray(t)&&!t.length?this._removeClass(this.uiDialog,"ui-dialog-buttons"):(V.each(t,function(t,e){var i;e=V.extend({type:"button"},e="function"==typeof e?{click:e,text:t}:e),i=e.click,t={icon:e.icon,iconPosition:e.iconPosition,showLabel:e.showLabel,icons:e.icons,text:e.text},delete e.click,delete e.icon,delete e.iconPosition,delete e.showLabel,delete e.icons,"boolean"==typeof e.text&&delete e.text,V("<button></button>",e).button(t).appendTo(s.uiButtonSet).on("click",function(){i.apply(s.element[0],arguments)})}),this._addClass(this.uiDialog,"ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog))},_makeDraggable:function(){var n=this,o=this.options;function a(t){return{position:t.position,offset:t.offset}}this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(t,e){n._addClass(V(this),"ui-dialog-dragging"),n._blockFrames(),n._trigger("dragStart",t,a(e))},drag:function(t,e){n._trigger("drag",t,a(e))},stop:function(t,e){var i=e.offset.left-n.document.scrollLeft(),s=e.offset.top-n.document.scrollTop();o.position={my:"left top",at:"left"+(0<=i?"+":"")+i+" top"+(0<=s?"+":"")+s,of:n.window},n._removeClass(V(this),"ui-dialog-dragging"),n._unblockFrames(),n._trigger("dragStop",t,a(e))}})},_makeResizable:function(){var n=this,o=this.options,t=o.resizable,e=this.uiDialog.css("position"),t="string"==typeof t?t:"n,e,s,w,se,sw,ne,nw";function a(t){return{originalPosition:t.originalPosition,originalSize:t.originalSize,position:t.position,size:t.size}}this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:o.maxWidth,maxHeight:o.maxHeight,minWidth:o.minWidth,minHeight:this._minHeight(),handles:t,start:function(t,e){n._addClass(V(this),"ui-dialog-resizing"),n._blockFrames(),n._trigger("resizeStart",t,a(e))},resize:function(t,e){n._trigger("resize",t,a(e))},stop:function(t,e){var i=n.uiDialog.offset(),s=i.left-n.document.scrollLeft(),i=i.top-n.document.scrollTop();o.height=n.uiDialog.height(),o.width=n.uiDialog.width(),o.position={my:"left top",at:"left"+(0<=s?"+":"")+s+" top"+(0<=i?"+":"")+i,of:n.window},n._removeClass(V(this),"ui-dialog-resizing"),n._unblockFrames(),n._trigger("resizeStop",t,a(e))}}).css("position",e)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=V(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),e=V.inArray(this,t);-1!==e&&t.splice(e,1)},_trackingInstances:function(){var t=this.document.data("ui-dialog-instances");return t||this.document.data("ui-dialog-instances",t=[]),t},_minHeight:function(){var t=this.options;return"auto"===t.height?t.minHeight:Math.min(t.minHeight,t.height)},_position:function(){var t=this.uiDialog.is(":visible");t||this.uiDialog.show(),this.uiDialog.position(this.options.position),t||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};V.each(t,function(t,e){i._setOption(t,e),t in i.sizeRelatedOptions&&(s=!0),t in i.resizableRelatedOptions&&(n[t]=e)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(t,e){var i,s=this.uiDialog;"disabled"!==t&&(this._super(t,e),"appendTo"===t&&this.uiDialog.appendTo(this._appendTo()),"buttons"===t&&this._createButtons(),"closeText"===t&&this.uiDialogTitlebarClose.button({label:V("<a>").text(""+this.options.closeText).html()}),"draggable"===t&&((i=s.is(":data(ui-draggable)"))&&!e&&s.draggable("destroy"),!i&&e&&this._makeDraggable()),"position"===t&&this._position(),"resizable"===t&&((i=s.is(":data(ui-resizable)"))&&!e&&s.resizable("destroy"),i&&"string"==typeof e&&s.resizable("option","handles",e),i||!1===e||this._makeResizable()),"title"===t&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var t,e,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),t=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),e=Math.max(0,s.minHeight-t),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-t):"none","auto"===s.height?this.element.css({minHeight:e,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-t)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=V(this);return V("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return!!V(t.target).closest(".ui-dialog").length||!!V(t.target).closest(".ui-datepicker").length},_createOverlay:function(){var i,s;this.options.modal&&(i=V.fn.jquery.substring(0,4),s=!0,this._delay(function(){s=!1}),this.document.data("ui-dialog-overlays")||this.document.on("focusin.ui-dialog",function(t){var e;s||((e=this._trackingInstances()[0])._allowInteraction(t)||(t.preventDefault(),e._focusTabbable(),"3.4."!==i&&"3.5."!==i||e._delay(e._restoreTabbableFocus)))}.bind(this)),this.overlay=V("<div>").appendTo(this._appendTo()),this._addClass(this.overlay,null,"ui-widget-overlay ui-front"),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1))},_destroyOverlay:function(){var t;this.options.modal&&this.overlay&&((t=this.document.data("ui-dialog-overlays")-1)?this.document.data("ui-dialog-overlays",t):(this.document.off("focusin.ui-dialog"),this.document.removeData("ui-dialog-overlays")),this.overlay.remove(),this.overlay=null)}}),!1!==V.uiBackCompat&&V.widget("ui.dialog",V.ui.dialog,{options:{dialogClass:""},_createWrapper:function(){this._super(),this.uiDialog.addClass(this.options.dialogClass)},_setOption:function(t,e){"dialogClass"===t&&this.uiDialog.removeClass(this.options.dialogClass).addClass(e),this._superApply(arguments)}});V.ui.dialog;function lt(t,e,i){return e<=t&&t<e+i}V.widget("ui.droppable",{version:"1.13.2",widgetEventPrefix:"drop",options:{accept:"*",addClasses:!0,greedy:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,e=this.options,i=e.accept;this.isover=!1,this.isout=!0,this.accept="function"==typeof i?i:function(t){return t.is(i)},this.proportions=function(){if(!arguments.length)return t=t||{width:this.element[0].offsetWidth,height:this.element[0].offsetHeight};t=arguments[0]},this._addToManager(e.scope),e.addClasses&&this._addClass("ui-droppable")},_addToManager:function(t){V.ui.ddmanager.droppables[t]=V.ui.ddmanager.droppables[t]||[],V.ui.ddmanager.droppables[t].push(this)},_splice:function(t){for(var e=0;e<t.length;e++)t[e]===this&&t.splice(e,1)},_destroy:function(){var t=V.ui.ddmanager.droppables[this.options.scope];this._splice(t)},_setOption:function(t,e){var i;"accept"===t?this.accept="function"==typeof e?e:function(t){return t.is(e)}:"scope"===t&&(i=V.ui.ddmanager.droppables[this.options.scope],this._splice(i),this._addToManager(e)),this._super(t,e)},_activate:function(t){var e=V.ui.ddmanager.current;this._addActiveClass(),e&&this._trigger("activate",t,this.ui(e))},_deactivate:function(t){var e=V.ui.ddmanager.current;this._removeActiveClass(),e&&this._trigger("deactivate",t,this.ui(e))},_over:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._addHoverClass(),this._trigger("over",t,this.ui(e)))},_out:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._removeHoverClass(),this._trigger("out",t,this.ui(e)))},_drop:function(e,t){var i=t||V.ui.ddmanager.current,s=!1;return!(!i||(i.currentItem||i.element)[0]===this.element[0])&&(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var t=V(this).droppable("instance");if(t.options.greedy&&!t.options.disabled&&t.options.scope===i.options.scope&&t.accept.call(t.element[0],i.currentItem||i.element)&&V.ui.intersect(i,V.extend(t,{offset:t.element.offset()}),t.options.tolerance,e))return!(s=!0)}),!s&&(!!this.accept.call(this.element[0],i.currentItem||i.element)&&(this._removeActiveClass(),this._removeHoverClass(),this._trigger("drop",e,this.ui(i)),this.element)))},ui:function(t){return{draggable:t.currentItem||t.element,helper:t.helper,position:t.position,offset:t.positionAbs}},_addHoverClass:function(){this._addClass("ui-droppable-hover")},_removeHoverClass:function(){this._removeClass("ui-droppable-hover")},_addActiveClass:function(){this._addClass("ui-droppable-active")},_removeActiveClass:function(){this._removeClass("ui-droppable-active")}}),V.ui.intersect=function(t,e,i,s){if(!e.offset)return!1;var n=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,a=n+t.helperProportions.width,r=o+t.helperProportions.height,l=e.offset.left,h=e.offset.top,c=l+e.proportions().width,u=h+e.proportions().height;switch(i){case"fit":return l<=n&&a<=c&&h<=o&&r<=u;case"intersect":return l<n+t.helperProportions.width/2&&a-t.helperProportions.width/2<c&&h<o+t.helperProportions.height/2&&r-t.helperProportions.height/2<u;case"pointer":return lt(s.pageY,h,e.proportions().height)&&lt(s.pageX,l,e.proportions().width);case"touch":return(h<=o&&o<=u||h<=r&&r<=u||o<h&&u<r)&&(l<=n&&n<=c||l<=a&&a<=c||n<l&&c<a);default:return!1}},!(V.ui.ddmanager={current:null,droppables:{default:[]},prepareOffsets:function(t,e){var i,s,n=V.ui.ddmanager.droppables[t.options.scope]||[],o=e?e.type:null,a=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();t:for(i=0;i<n.length;i++)if(!(n[i].options.disabled||t&&!n[i].accept.call(n[i].element[0],t.currentItem||t.element))){for(s=0;s<a.length;s++)if(a[s]===n[i].element[0]){n[i].proportions().height=0;continue t}n[i].visible="none"!==n[i].element.css("display"),n[i].visible&&("mousedown"===o&&n[i]._activate.call(n[i],e),n[i].offset=n[i].element.offset(),n[i].proportions({width:n[i].element[0].offsetWidth,height:n[i].element[0].offsetHeight}))}},drop:function(t,e){var i=!1;return V.each((V.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&V.ui.intersect(t,this,this.options.tolerance,e)&&(i=this._drop.call(this,e)||i),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,e)))}),i},dragStart:function(t,e){t.element.parentsUntil("body").on("scroll.droppable",function(){t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)})},drag:function(n,o){n.options.refreshPositions&&V.ui.ddmanager.prepareOffsets(n,o),V.each(V.ui.ddmanager.droppables[n.options.scope]||[],function(){var t,e,i,s;this.options.disabled||this.greedyChild||!this.visible||(s=!(i=V.ui.intersect(n,this,this.options.tolerance,o))&&this.isover?"isout":i&&!this.isover?"isover":null)&&(this.options.greedy&&(e=this.options.scope,(i=this.element.parents(":data(ui-droppable)").filter(function(){return V(this).droppable("instance").options.scope===e})).length&&((t=V(i[0]).droppable("instance")).greedyChild="isover"===s)),t&&"isover"===s&&(t.isover=!1,t.isout=!0,t._out.call(t,o)),this[s]=!0,this["isout"===s?"isover":"isout"]=!1,this["isover"===s?"_over":"_out"].call(this,o),t&&"isout"===s&&(t.isout=!1,t.isover=!0,t._over.call(t,o)))})},dragStop:function(t,e){t.element.parentsUntil("body").off("scroll.droppable"),t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)}})!==V.uiBackCompat&&V.widget("ui.droppable",V.ui.droppable,{options:{hoverClass:!1,activeClass:!1},_addActiveClass:function(){this._super(),this.options.activeClass&&this.element.addClass(this.options.activeClass)},_removeActiveClass:function(){this._super(),this.options.activeClass&&this.element.removeClass(this.options.activeClass)},_addHoverClass:function(){this._super(),this.options.hoverClass&&this.element.addClass(this.options.hoverClass)},_removeHoverClass:function(){this._super(),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass)}});V.ui.droppable,V.widget("ui.progressbar",{version:"1.13.2",options:{classes:{"ui-progressbar":"ui-corner-all","ui-progressbar-value":"ui-corner-left","ui-progressbar-complete":"ui-corner-right"},max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.attr({role:"progressbar","aria-valuemin":this.min}),this._addClass("ui-progressbar","ui-widget ui-widget-content"),this.valueDiv=V("<div>").appendTo(this.element),this._addClass(this.valueDiv,"ui-progressbar-value","ui-widget-header"),this._refreshValue()},_destroy:function(){this.element.removeAttr("role aria-valuemin aria-valuemax aria-valuenow"),this.valueDiv.remove()},value:function(t){if(void 0===t)return this.options.value;this.options.value=this._constrainedValue(t),this._refreshValue()},_constrainedValue:function(t){return void 0===t&&(t=this.options.value),this.indeterminate=!1===t,"number"!=typeof t&&(t=0),!this.indeterminate&&Math.min(this.options.max,Math.max(this.min,t))},_setOptions:function(t){var e=t.value;delete t.value,this._super(t),this.options.value=this._constrainedValue(e),this._refreshValue()},_setOption:function(t,e){"max"===t&&(e=Math.max(this.min,e)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,e=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).width(e.toFixed(0)+"%"),this._toggleClass(this.valueDiv,"ui-progressbar-complete",null,t===this.options.max)._toggleClass("ui-progressbar-indeterminate",null,this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=V("<div>").appendTo(this.valueDiv),this._addClass(this.overlayDiv,"ui-progressbar-overlay"))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),V.widget("ui.selectable",V.ui.mouse,{version:"1.13.2",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var i=this;this._addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){i.elementPos=V(i.element[0]).offset(),i.selectees=V(i.options.filter,i.element[0]),i._addClass(i.selectees,"ui-selectee"),i.selectees.each(function(){var t=V(this),e=t.offset(),e={left:e.left-i.elementPos.left,top:e.top-i.elementPos.top};V.data(this,"selectable-item",{element:this,$element:t,left:e.left,top:e.top,right:e.left+t.outerWidth(),bottom:e.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this._mouseInit(),this.helper=V("<div>"),this._addClass(this.helper,"ui-selectable-helper")},_destroy:function(){this.selectees.removeData("selectable-item"),this._mouseDestroy()},_mouseStart:function(i){var s=this,t=this.options;this.opos=[i.pageX,i.pageY],this.elementPos=V(this.element[0]).offset(),this.options.disabled||(this.selectees=V(t.filter,this.element[0]),this._trigger("start",i),V(t.appendTo).append(this.helper),this.helper.css({left:i.pageX,top:i.pageY,width:0,height:0}),t.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var t=V.data(this,"selectable-item");t.startselected=!0,i.metaKey||i.ctrlKey||(s._removeClass(t.$element,"ui-selected"),t.selected=!1,s._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,s._trigger("unselecting",i,{unselecting:t.element}))}),V(i.target).parents().addBack().each(function(){var t,e=V.data(this,"selectable-item");if(e)return t=!i.metaKey&&!i.ctrlKey||!e.$element.hasClass("ui-selected"),s._removeClass(e.$element,t?"ui-unselecting":"ui-selected")._addClass(e.$element,t?"ui-selecting":"ui-unselecting"),e.unselecting=!t,e.selecting=t,(e.selected=t)?s._trigger("selecting",i,{selecting:e.element}):s._trigger("unselecting",i,{unselecting:e.element}),!1}))},_mouseDrag:function(s){if(this.dragged=!0,!this.options.disabled){var t,n=this,o=this.options,a=this.opos[0],r=this.opos[1],l=s.pageX,h=s.pageY;return l<a&&(t=l,l=a,a=t),h<r&&(t=h,h=r,r=t),this.helper.css({left:a,top:r,width:l-a,height:h-r}),this.selectees.each(function(){var t=V.data(this,"selectable-item"),e=!1,i={};t&&t.element!==n.element[0]&&(i.left=t.left+n.elementPos.left,i.right=t.right+n.elementPos.left,i.top=t.top+n.elementPos.top,i.bottom=t.bottom+n.elementPos.top,"touch"===o.tolerance?e=!(i.left>l||i.right<a||i.top>h||i.bottom<r):"fit"===o.tolerance&&(e=i.left>a&&i.right<l&&i.top>r&&i.bottom<h),e?(t.selected&&(n._removeClass(t.$element,"ui-selected"),t.selected=!1),t.unselecting&&(n._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1),t.selecting||(n._addClass(t.$element,"ui-selecting"),t.selecting=!0,n._trigger("selecting",s,{selecting:t.element}))):(t.selecting&&((s.metaKey||s.ctrlKey)&&t.startselected?(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,n._addClass(t.$element,"ui-selected"),t.selected=!0):(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,t.startselected&&(n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0),n._trigger("unselecting",s,{unselecting:t.element}))),t.selected&&(s.metaKey||s.ctrlKey||t.startselected||(n._removeClass(t.$element,"ui-selected"),t.selected=!1,n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,n._trigger("unselecting",s,{unselecting:t.element})))))}),!1}},_mouseStop:function(e){var i=this;return this.dragged=!1,V(".ui-unselecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1,t.startselected=!1,i._trigger("unselected",e,{unselected:t.element})}),V(".ui-selecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-selecting")._addClass(t.$element,"ui-selected"),t.selecting=!1,t.selected=!0,t.startselected=!0,i._trigger("selected",e,{selected:t.element})}),this._trigger("stop",e),this.helper.remove(),!1}}),V.widget("ui.selectmenu",[V.ui.formResetMixin,{version:"1.13.2",defaultElement:"<select>",options:{appendTo:null,classes:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"},disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:!1,change:null,close:null,focus:null,open:null,select:null},_create:function(){var t=this.element.uniqueId().attr("id");this.ids={element:t,button:t+"-button",menu:t+"-menu"},this._drawButton(),this._drawMenu(),this._bindFormResetHandler(),this._rendered=!1,this.menuItems=V()},_drawButton:function(){var t,e=this,i=this._parseOption(this.element.find("option:selected"),this.element[0].selectedIndex);this.labels=this.element.labels().attr("for",this.ids.button),this._on(this.labels,{click:function(t){this.button.trigger("focus"),t.preventDefault()}}),this.element.hide(),this.button=V("<span>",{tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true",title:this.element.attr("title")}).insertAfter(this.element),this._addClass(this.button,"ui-selectmenu-button ui-selectmenu-button-closed","ui-button ui-widget"),t=V("<span>").appendTo(this.button),this._addClass(t,"ui-selectmenu-icon","ui-icon "+this.options.icons.button),this.buttonItem=this._renderButtonItem(i).appendTo(this.button),!1!==this.options.width&&this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){e._rendered||e._refreshMenu()})},_drawMenu:function(){var i=this;this.menu=V("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=V("<div>").append(this.menu),this._addClass(this.menuWrap,"ui-selectmenu-menu","ui-front"),this.menuWrap.appendTo(this._appendTo()),this.menuInstance=this.menu.menu({classes:{"ui-menu":"ui-corner-bottom"},role:"listbox",select:function(t,e){t.preventDefault(),i._setSelection(),i._select(e.item.data("ui-selectmenu-item"),t)},focus:function(t,e){e=e.item.data("ui-selectmenu-item");null!=i.focusIndex&&e.index!==i.focusIndex&&(i._trigger("focus",t,{item:e}),i.isOpen||i._select(e,t)),i.focusIndex=e.index,i.button.attr("aria-activedescendant",i.menuItems.eq(e.index).attr("id"))}}).menu("instance"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(this._getSelectedItem().data("ui-selectmenu-item")||{})),null===this.options.width&&this._resizeButton()},_refreshMenu:function(){var t=this.element.find("option");this.menu.empty(),this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup").find(".ui-menu-item-wrapper"),this._rendered=!0,t.length&&(t=this._getSelectedItem(),this.menuInstance.focus(null,t),this._setAria(t.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(t){this.options.disabled||(this._rendered?(this._removeClass(this.menu.find(".ui-state-active"),null,"ui-state-active"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.menuItems.length&&(this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",t)))},_position:function(){this.menuWrap.position(V.extend({of:this.button},this.options.position))},close:function(t){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",t))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderButtonItem:function(t){var e=V("<span>");return this._setText(e,t.label),this._addClass(e,"ui-selectmenu-text"),e},_renderMenu:function(s,t){var n=this,o="";V.each(t,function(t,e){var i;e.optgroup!==o&&(i=V("<li>",{text:e.optgroup}),n._addClass(i,"ui-selectmenu-optgroup","ui-menu-divider"+(e.element.parent("optgroup").prop("disabled")?" ui-state-disabled":"")),i.appendTo(s),o=e.optgroup),n._renderItemData(s,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-selectmenu-item",e)},_renderItem:function(t,e){var i=V("<li>"),s=V("<div>",{title:e.element.attr("title")});return e.disabled&&this._addClass(i,null,"ui-state-disabled"),this._setText(s,e.label),i.append(s).appendTo(t)},_setText:function(t,e){e?t.text(e):t.html("&#160;")},_move:function(t,e){var i,s=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex).parent("li"):(i=this.menuItems.eq(this.element[0].selectedIndex).parent("li"),s+=":not(.ui-state-disabled)"),(s="first"===t||"last"===t?i["first"===t?"prevAll":"nextAll"](s).eq(-1):i[t+"All"](s).eq(0)).length&&this.menuInstance.focus(e,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex).parent("li")},_toggle:function(t){this[this.isOpen?"close":"open"](t)},_setSelection:function(){var t;this.range&&(window.getSelection?((t=window.getSelection()).removeAllRanges(),t.addRange(this.range)):this.range.select(),this.button.trigger("focus"))},_documentClick:{mousedown:function(t){this.isOpen&&(V(t.target).closest(".ui-selectmenu-menu, #"+V.escapeSelector(this.ids.button)).length||this.close(t))}},_buttonEvents:{mousedown:function(){var t;window.getSelection?(t=window.getSelection()).rangeCount&&(this.range=t.getRangeAt(0)):this.range=document.selection.createRange()},click:function(t){this._setSelection(),this._toggle(t)},keydown:function(t){var e=!0;switch(t.keyCode){case V.ui.keyCode.TAB:case V.ui.keyCode.ESCAPE:this.close(t),e=!1;break;case V.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case V.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case V.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case V.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case V.ui.keyCode.LEFT:this._move("prev",t);break;case V.ui.keyCode.RIGHT:this._move("next",t);break;case V.ui.keyCode.HOME:case V.ui.keyCode.PAGE_UP:this._move("first",t);break;case V.ui.keyCode.END:case V.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),e=!1}e&&t.preventDefault()}},_selectFocusedItem:function(t){var e=this.menuItems.eq(this.focusIndex).parent("li");e.hasClass("ui-state-disabled")||this._select(e.data("ui-selectmenu-item"),t)},_select:function(t,e){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=t.index,this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(t)),this._setAria(t),this._trigger("select",e,{item:t}),t.index!==i&&this._trigger("change",e,{item:t}),this.close(e)},_setAria:function(t){t=this.menuItems.eq(t.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(t,e){var i;"icons"===t&&(i=this.button.find("span.ui-icon"),this._removeClass(i,null,this.options.icons.button)._addClass(i,null,e.button)),this._super(t,e),"appendTo"===t&&this.menuWrap.appendTo(this._appendTo()),"width"===t&&this._resizeButton()},_setOptionDisabled:function(t){this._super(t),this.menuInstance.option("disabled",t),this.button.attr("aria-disabled",t),this._toggleClass(this.button,null,"ui-state-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_toggleAttr:function(){this.button.attr("aria-expanded",this.isOpen),this._removeClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"closed":"open"))._addClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"open":"closed"))._toggleClass(this.menuWrap,"ui-selectmenu-open",null,this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var t=this.options.width;!1!==t?(null===t&&(t=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(t)):this.button.css("width","")},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){var t=this._super();return t.disabled=this.element.prop("disabled"),t},_parseOptions:function(t){var i=this,s=[];t.each(function(t,e){e.hidden||s.push(i._parseOption(V(e),t))}),this.items=s},_parseOption:function(t,e){var i=t.parent("optgroup");return{element:t,index:e,value:t.val(),label:t.text(),optgroup:i.attr("label")||"",disabled:i.prop("disabled")||t.prop("disabled")}},_destroy:function(){this._unbindFormResetHandler(),this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.labels.attr("for",this.ids.element)}}]),V.widget("ui.slider",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"slide",options:{animate:!1,classes:{"ui-slider":"ui-corner-all","ui-slider-handle":"ui-corner-all","ui-slider-range":"ui-corner-all ui-widget-header"},distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this._addClass("ui-slider ui-slider-"+this.orientation,"ui-widget ui-widget-content"),this._refresh(),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,e=this.options,i=this.element.find(".ui-slider-handle"),s=[],n=e.values&&e.values.length||1;for(i.length>n&&(i.slice(n).remove(),i=i.slice(0,n)),t=i.length;t<n;t++)s.push("<span tabindex='0'></span>");this.handles=i.add(V(s.join("")).appendTo(this.element)),this._addClass(this.handles,"ui-slider-handle","ui-state-default"),this.handle=this.handles.eq(0),this.handles.each(function(t){V(this).data("ui-slider-handle-index",t).attr("tabIndex",0)})},_createRange:function(){var t=this.options;t.range?(!0===t.range&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:Array.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?(this._removeClass(this.range,"ui-slider-range-min ui-slider-range-max"),this.range.css({left:"",bottom:""})):(this.range=V("<div>").appendTo(this.element),this._addClass(this.range,"ui-slider-range")),"min"!==t.range&&"max"!==t.range||this._addClass(this.range,"ui-slider-range-"+t.range)):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,o,e,a,r=this,l=this.options;return!l.disabled&&(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),a={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(a),s=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var e=Math.abs(i-r.values(t));(e<s||s===e&&(t===r._lastChangedValue||r.values(t)===l.min))&&(s=e,n=V(this),o=t)}),!1!==this._start(t,o)&&(this._mouseSliding=!0,this._handleIndex=o,this._addClass(n,null,"ui-state-active"),n.trigger("focus"),e=n.offset(),a=!V(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=a?{left:0,top:0}:{left:t.pageX-e.left-n.width()/2,top:t.pageY-e.top-n.height()/2-(parseInt(n.css("borderTopWidth"),10)||0)-(parseInt(n.css("borderBottomWidth"),10)||0)+(parseInt(n.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,i),this._animateOff=!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},e=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,e),!1},_mouseStop:function(t){return this._removeClass(this.handles,null,"ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,t="horizontal"===this.orientation?(e=this.elementSize.width,t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),t=t/e;return(t=1<t?1:t)<0&&(t=0),"vertical"===this.orientation&&(t=1-t),e=this._valueMax()-this._valueMin(),e=this._valueMin()+t*e,this._trimAlignValue(e)},_uiHash:function(t,e,i){var s={handle:this.handles[t],handleIndex:t,value:void 0!==e?e:this.value()};return this._hasMultipleValues()&&(s.value=void 0!==e?e:this.values(t),s.values=i||this.values()),s},_hasMultipleValues:function(){return this.options.values&&this.options.values.length},_start:function(t,e){return this._trigger("start",t,this._uiHash(e))},_slide:function(t,e,i){var s,n=this.value(),o=this.values();this._hasMultipleValues()&&(s=this.values(e?0:1),n=this.values(e),2===this.options.values.length&&!0===this.options.range&&(i=0===e?Math.min(s,i):Math.max(s,i)),o[e]=i),i!==n&&!1!==this._trigger("slide",t,this._uiHash(e,i,o))&&(this._hasMultipleValues()?this.values(e,i):this.value(i))},_stop:function(t,e){this._trigger("stop",t,this._uiHash(e))},_change:function(t,e){this._keySliding||this._mouseSliding||(this._lastChangedValue=e,this._trigger("change",t,this._uiHash(e)))},value:function(t){return arguments.length?(this.options.value=this._trimAlignValue(t),this._refreshValue(),void this._change(null,0)):this._value()},values:function(t,e){var i,s,n;if(1<arguments.length)return this.options.values[t]=this._trimAlignValue(e),this._refreshValue(),void this._change(null,t);if(!arguments.length)return this._values();if(!Array.isArray(t))return this._hasMultipleValues()?this._values(t):this.value();for(i=this.options.values,s=t,n=0;n<i.length;n+=1)i[n]=this._trimAlignValue(s[n]),this._change(null,n);this._refreshValue()},_setOption:function(t,e){var i,s=0;switch("range"===t&&!0===this.options.range&&("min"===e?(this.options.value=this._values(0),this.options.values=null):"max"===e&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),Array.isArray(this.options.values)&&(s=this.options.values.length),this._super(t,e),t){case"orientation":this._detectOrientation(),this._removeClass("ui-slider-horizontal ui-slider-vertical")._addClass("ui-slider-"+this.orientation),this._refreshValue(),this.options.range&&this._refreshRange(e),this.handles.css("horizontal"===e?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),i=s-1;0<=i;i--)this._change(null,i);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_setOptionDisabled:function(t){this._super(t),this._toggleClass(null,"ui-state-disabled",!!t)},_value:function(){var t=this.options.value;return t=this._trimAlignValue(t)},_values:function(t){var e,i;if(arguments.length)return t=this.options.values[t],t=this._trimAlignValue(t);if(this._hasMultipleValues()){for(e=this.options.values.slice(),i=0;i<e.length;i+=1)e[i]=this._trimAlignValue(e[i]);return e}return[]},_trimAlignValue:function(t){if(t<=this._valueMin())return this._valueMin();if(t>=this._valueMax())return this._valueMax();var e=0<this.options.step?this.options.step:1,i=(t-this._valueMin())%e,t=t-i;return 2*Math.abs(i)>=e&&(t+=0<i?e:-e),parseFloat(t.toFixed(5))},_calculateNewMax:function(){var t=this.options.max,e=this._valueMin(),i=this.options.step;(t=Math.round((t-e)/i)*i+e)>this.options.max&&(t-=i),this.max=parseFloat(t.toFixed(this._precision()))},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshRange:function(t){"vertical"===t&&this.range.css({width:"",left:""}),"horizontal"===t&&this.range.css({height:"",bottom:""})},_refreshValue:function(){var e,i,t,s,n,o=this.options.range,a=this.options,r=this,l=!this._animateOff&&a.animate,h={};this._hasMultipleValues()?this.handles.each(function(t){i=(r.values(t)-r._valueMin())/(r._valueMax()-r._valueMin())*100,h["horizontal"===r.orientation?"left":"bottom"]=i+"%",V(this).stop(1,1)[l?"animate":"css"](h,a.animate),!0===r.options.range&&("horizontal"===r.orientation?(0===t&&r.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({width:i-e+"%"},{queue:!1,duration:a.animate})):(0===t&&r.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({height:i-e+"%"},{queue:!1,duration:a.animate}))),e=i}):(t=this.value(),s=this._valueMin(),n=this._valueMax(),i=n!==s?(t-s)/(n-s)*100:0,h["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](h,a.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},a.animate),"max"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:100-i+"%"},a.animate),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},a.animate),"max"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:100-i+"%"},a.animate))},_handleEvents:{keydown:function(t){var e,i,s,n=V(t.target).data("ui-slider-handle-index");switch(t.keyCode){case V.ui.keyCode.HOME:case V.ui.keyCode.END:case V.ui.keyCode.PAGE_UP:case V.ui.keyCode.PAGE_DOWN:case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,this._addClass(V(t.target),null,"ui-state-active"),!1===this._start(t,n)))return}switch(s=this.options.step,e=i=this._hasMultipleValues()?this.values(n):this.value(),t.keyCode){case V.ui.keyCode.HOME:i=this._valueMin();break;case V.ui.keyCode.END:i=this._valueMax();break;case V.ui.keyCode.PAGE_UP:i=this._trimAlignValue(e+(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.PAGE_DOWN:i=this._trimAlignValue(e-(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:if(e===this._valueMax())return;i=this._trimAlignValue(e+s);break;case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(e===this._valueMin())return;i=this._trimAlignValue(e-s)}this._slide(t,n,i)},keyup:function(t){var e=V(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,e),this._change(t,e),this._removeClass(V(t.target),null,"ui-state-active"))}}}),V.widget("ui.sortable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(t,e,i){return e<=t&&t<e+i},_isFloating:function(t){return/left|right/.test(t.css("float"))||/inline|table-cell/.test(t.css("display"))},_create:function(){this.containerCache={},this._addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(t,e){this._super(t,e),"handle"===t&&this._setHandleClassName()},_setHandleClassName:function(){var t=this;this._removeClass(this.element.find(".ui-sortable-handle"),"ui-sortable-handle"),V.each(this.items,function(){t._addClass(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item,"ui-sortable-handle")})},_destroy:function(){this._mouseDestroy();for(var t=this.items.length-1;0<=t;t--)this.items[t].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,e){var i=null,s=!1,n=this;return!this.reverting&&(!this.options.disabled&&"static"!==this.options.type&&(this._refreshItems(t),V(t.target).parents().each(function(){if(V.data(this,n.widgetName+"-item")===n)return i=V(this),!1}),!!(i=V.data(t.target,n.widgetName+"-item")===n?V(t.target):i)&&(!(this.options.handle&&!e&&(V(this.options.handle,i).find("*").addBack().each(function(){this===t.target&&(s=!0)}),!s))&&(this.currentItem=i,this._removeCurrentsFromItems(),!0))))},_mouseStart:function(t,e,i){var s,n,o=this.options;if((this.currentContainer=this).refreshPositions(),this.appendTo=V("parent"!==o.appendTo?o.appendTo:this.currentItem.parent()),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},V.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),this.scrollParent=this.placeholder.scrollParent(),V.extend(this.offset,{parent:this._getParentOffset()}),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(n=this.document.find("body"),this.storedCursor=n.css("cursor"),n.css("cursor",o.cursor),this.storedStylesheet=V("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(n)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!i)for(s=this.containers.length-1;0<=s;s--)this.containers[s]._trigger("activate",t,this._uiHash(this));return V.ui.ddmanager&&(V.ui.ddmanager.current=this),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this._addClass(this.helper,"ui-sortable-helper"),this.helper.parent().is(this.appendTo)||(this.helper.detach().appendTo(this.appendTo),this.offset.parent=this._getParentOffset()),this.position=this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,this.lastPositionAbs=this.positionAbs=this._convertPositionTo("absolute"),this._mouseDrag(t),!0},_scroll:function(t){var e=this.options,i=!1;return this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<e.scrollSensitivity?this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop+e.scrollSpeed:t.pageY-this.overflowOffset.top<e.scrollSensitivity&&(this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop-e.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<e.scrollSensitivity?this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft+e.scrollSpeed:t.pageX-this.overflowOffset.left<e.scrollSensitivity&&(this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft-e.scrollSpeed)):(t.pageY-this.document.scrollTop()<e.scrollSensitivity?i=this.document.scrollTop(this.document.scrollTop()-e.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<e.scrollSensitivity&&(i=this.document.scrollTop(this.document.scrollTop()+e.scrollSpeed)),t.pageX-this.document.scrollLeft()<e.scrollSensitivity?i=this.document.scrollLeft(this.document.scrollLeft()-e.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<e.scrollSensitivity&&(i=this.document.scrollLeft(this.document.scrollLeft()+e.scrollSpeed))),i},_mouseDrag:function(t){var e,i,s,n,o=this.options;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),o.scroll&&!1!==this._scroll(t)&&(this._refreshItemPositions(!0),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t)),this.dragDirection={vertical:this._getDragVerticalDirection(),horizontal:this._getDragHorizontalDirection()},e=this.items.length-1;0<=e;e--)if(s=(i=this.items[e]).item[0],(n=this._intersectsWithPointer(i))&&i.instance===this.currentContainer&&!(s===this.currentItem[0]||this.placeholder[1===n?"next":"prev"]()[0]===s||V.contains(this.placeholder[0],s)||"semi-dynamic"===this.options.type&&V.contains(this.element[0],s))){if(this.direction=1===n?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(i))break;this._rearrange(t,i),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),V.ui.ddmanager&&V.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,e){var i,s,n,o;if(t)return V.ui.ddmanager&&!this.options.dropBehaviour&&V.ui.ddmanager.drop(this,t),this.options.revert?(s=(i=this).placeholder.offset(),o={},(n=this.options.axis)&&"x"!==n||(o.left=s.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),n&&"y"!==n||(o.top=s.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,V(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){i._clear(t)})):this._clear(t,e),!1},cancel:function(){if(this.dragging){this._mouseUp(new V.Event("mouseup",{target:null})),"original"===this.options.helper?(this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")):this.currentItem.show();for(var t=this.containers.length-1;0<=t;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),V.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?V(this.domPosition.prev).after(this.currentItem):V(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(e){var t=this._getItemsAsjQuery(e&&e.connected),i=[];return e=e||{},V(t).each(function(){var t=(V(e.item||this).attr(e.attribute||"id")||"").match(e.expression||/(.+)[\-=_](.+)/);t&&i.push((e.key||t[1]+"[]")+"="+(e.key&&e.expression?t[1]:t[2]))}),!i.length&&e.key&&i.push(e.key+"="),i.join("&")},toArray:function(t){var e=this._getItemsAsjQuery(t&&t.connected),i=[];return t=t||{},e.each(function(){i.push(V(t.item||this).attr(t.attribute||"id")||"")}),i},_intersectsWith:function(t){var e=this.positionAbs.left,i=e+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,o=t.left,a=o+t.width,r=t.top,l=r+t.height,h=this.offset.click.top,c=this.offset.click.left,h="x"===this.options.axis||r<s+h&&s+h<l,c="y"===this.options.axis||o<e+c&&e+c<a;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>t[this.floating?"width":"height"]?h&&c:o<e+this.helperProportions.width/2&&i-this.helperProportions.width/2<a&&r<s+this.helperProportions.height/2&&n-this.helperProportions.height/2<l},_intersectsWithPointer:function(t){var e="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),t="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width);return!(!e||!t)&&(e=this.dragDirection.vertical,t=this.dragDirection.horizontal,this.floating?"right"===t||"down"===e?2:1:e&&("down"===e?2:1))},_intersectsWithSides:function(t){var e=this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),s=this.dragDirection.vertical,t=this.dragDirection.horizontal;return this.floating&&t?"right"===t&&i||"left"===t&&!i:s&&("down"===s&&e||"up"===s&&!e)},_getDragVerticalDirection:function(){var t=this.positionAbs.top-this.lastPositionAbs.top;return 0!=t&&(0<t?"down":"up")},_getDragHorizontalDirection:function(){var t=this.positionAbs.left-this.lastPositionAbs.left;return 0!=t&&(0<t?"right":"left")},refresh:function(t){return this._refreshItems(t),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var t=this.options;return t.connectWith.constructor===String?[t.connectWith]:t.connectWith},_getItemsAsjQuery:function(t){var e,i,s,n,o=[],a=[],r=this._connectWith();if(r&&t)for(e=r.length-1;0<=e;e--)for(i=(s=V(r[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&a.push(["function"==typeof n.options.items?n.options.items.call(n.element):V(n.options.items,n.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),n]);function l(){o.push(this)}for(a.push(["function"==typeof this.options.items?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):V(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),e=a.length-1;0<=e;e--)a[e][0].each(l);return V(o)},_removeCurrentsFromItems:function(){var i=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=V.grep(this.items,function(t){for(var e=0;e<i.length;e++)if(i[e]===t.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var e,i,s,n,o,a,r,l,h=this.items,c=[["function"==typeof this.options.items?this.options.items.call(this.element[0],t,{item:this.currentItem}):V(this.options.items,this.element),this]],u=this._connectWith();if(u&&this.ready)for(e=u.length-1;0<=e;e--)for(i=(s=V(u[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&(c.push(["function"==typeof n.options.items?n.options.items.call(n.element[0],t,{item:this.currentItem}):V(n.options.items,n.element),n]),this.containers.push(n));for(e=c.length-1;0<=e;e--)for(o=c[e][1],l=(a=c[e][i=0]).length;i<l;i++)(r=V(a[i])).data(this.widgetName+"-item",o),h.push({item:r,instance:o,width:0,height:0,left:0,top:0})},_refreshItemPositions:function(t){for(var e,i,s=this.items.length-1;0<=s;s--)e=this.items[s],this.currentContainer&&e.instance!==this.currentContainer&&e.item[0]!==this.currentItem[0]||(i=this.options.toleranceElement?V(this.options.toleranceElement,e.item):e.item,t||(e.width=i.outerWidth(),e.height=i.outerHeight()),i=i.offset(),e.left=i.left,e.top=i.top)},refreshPositions:function(t){var e,i;if(this.floating=!!this.items.length&&("x"===this.options.axis||this._isFloating(this.items[0].item)),this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset()),this._refreshItemPositions(t),this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(e=this.containers.length-1;0<=e;e--)i=this.containers[e].element.offset(),this.containers[e].containerCache.left=i.left,this.containers[e].containerCache.top=i.top,this.containers[e].containerCache.width=this.containers[e].element.outerWidth(),this.containers[e].containerCache.height=this.containers[e].element.outerHeight();return this},_createPlaceholder:function(i){var s,n,o=(i=i||this).options;o.placeholder&&o.placeholder.constructor!==String||(s=o.placeholder,n=i.currentItem[0].nodeName.toLowerCase(),o.placeholder={element:function(){var t=V("<"+n+">",i.document[0]);return i._addClass(t,"ui-sortable-placeholder",s||i.currentItem[0].className)._removeClass(t,"ui-sortable-helper"),"tbody"===n?i._createTrPlaceholder(i.currentItem.find("tr").eq(0),V("<tr>",i.document[0]).appendTo(t)):"tr"===n?i._createTrPlaceholder(i.currentItem,t):"img"===n&&t.attr("src",i.currentItem.attr("src")),s||t.css("visibility","hidden"),t},update:function(t,e){s&&!o.forcePlaceholderSize||(e.height()&&(!o.forcePlaceholderSize||"tbody"!==n&&"tr"!==n)||e.height(i.currentItem.innerHeight()-parseInt(i.currentItem.css("paddingTop")||0,10)-parseInt(i.currentItem.css("paddingBottom")||0,10)),e.width()||e.width(i.currentItem.innerWidth()-parseInt(i.currentItem.css("paddingLeft")||0,10)-parseInt(i.currentItem.css("paddingRight")||0,10)))}}),i.placeholder=V(o.placeholder.element.call(i.element,i.currentItem)),i.currentItem.after(i.placeholder),o.placeholder.update(i,i.placeholder)},_createTrPlaceholder:function(t,e){var i=this;t.children().each(function(){V("<td>&#160;</td>",i.document[0]).attr("colspan",V(this).attr("colspan")||1).appendTo(e)})},_contactContainers:function(t){for(var e,i,s,n,o,a,r,l,h,c=null,u=null,d=this.containers.length-1;0<=d;d--)V.contains(this.currentItem[0],this.containers[d].element[0])||(this._intersectsWith(this.containers[d].containerCache)?c&&V.contains(this.containers[d].element[0],c.element[0])||(c=this.containers[d],u=d):this.containers[d].containerCache.over&&(this.containers[d]._trigger("out",t,this._uiHash(this)),this.containers[d].containerCache.over=0));if(c)if(1===this.containers.length)this.containers[u].containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1);else{for(i=1e4,s=null,n=(l=c.floating||this._isFloating(this.currentItem))?"left":"top",o=l?"width":"height",h=l?"pageX":"pageY",e=this.items.length-1;0<=e;e--)V.contains(this.containers[u].element[0],this.items[e].item[0])&&this.items[e].item[0]!==this.currentItem[0]&&(a=this.items[e].item.offset()[n],r=!1,t[h]-a>this.items[e][o]/2&&(r=!0),Math.abs(t[h]-a)<i&&(i=Math.abs(t[h]-a),s=this.items[e],this.direction=r?"up":"down"));(s||this.options.dropOnEmpty)&&(this.currentContainer!==this.containers[u]?(s?this._rearrange(t,s,null,!0):this._rearrange(t,null,this.containers[u].element,!0),this._trigger("change",t,this._uiHash()),this.containers[u]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[u],this.options.placeholder.update(this.currentContainer,this.placeholder),this.scrollParent=this.placeholder.scrollParent(),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1):this.currentContainer.containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1))}},_createHelper:function(t){var e=this.options,t="function"==typeof e.helper?V(e.helper.apply(this.element[0],[t,this.currentItem])):"clone"===e.helper?this.currentItem.clone():this.currentItem;return t.parents("body").length||this.appendTo[0].appendChild(t[0]),t[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),t[0].style.width&&!e.forceHelperSize||t.width(this.currentItem.width()),t[0].style.height&&!e.forceHelperSize||t.height(this.currentItem.height()),t},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&V.ui.ie?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i=this.options;"parent"===i.containment&&(i.containment=this.helper[0].parentNode),"document"!==i.containment&&"window"!==i.containment||(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===i.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===i.containment?this.document.height()||document.body.parentNode.scrollHeight:this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(i.containment)||(t=V(i.containment)[0],e=V(i.containment).offset(),i="hidden"!==V(t).css("overflow"),this.containment=[e.left+(parseInt(V(t).css("borderLeftWidth"),10)||0)+(parseInt(V(t).css("paddingLeft"),10)||0)-this.margins.left,e.top+(parseInt(V(t).css("borderTopWidth"),10)||0)+(parseInt(V(t).css("paddingTop"),10)||0)-this.margins.top,e.left+(i?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(V(t).css("borderLeftWidth"),10)||0)-(parseInt(V(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,e.top+(i?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(V(t).css("borderTopWidth"),10)||0)-(parseInt(V(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,s="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,t=/(html|body)/i.test(s[0].tagName);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():t?0:s.scrollTop())*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():t?0:s.scrollLeft())*i}},_generatePosition:function(t){var e=this.options,i=t.pageX,s=t.pageY,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(n[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(i=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(s=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(i=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(s=this.containment[3]+this.offset.click.top)),e.grid&&(t=this.originalPageY+Math.round((s-this.originalPageY)/e.grid[1])*e.grid[1],s=!this.containment||t-this.offset.click.top>=this.containment[1]&&t-this.offset.click.top<=this.containment[3]?t:t-this.offset.click.top>=this.containment[1]?t-e.grid[1]:t+e.grid[1],t=this.originalPageX+Math.round((i-this.originalPageX)/e.grid[0])*e.grid[0],i=!this.containment||t-this.offset.click.left>=this.containment[0]&&t-this.offset.click.left<=this.containment[2]?t:t-this.offset.click.left>=this.containment[0]?t-e.grid[0]:t+e.grid[0])),{top:s-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():o?0:n.scrollTop()),left:i-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():o?0:n.scrollLeft())}},_rearrange:function(t,e,i,s){i?i[0].appendChild(this.placeholder[0]):e.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?e.item[0]:e.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(t,e){this.reverting=!1;var i,s=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(i in this._storedCSS)"auto"!==this._storedCSS[i]&&"static"!==this._storedCSS[i]||(this._storedCSS[i]="");this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")}else this.currentItem.show();function n(e,i,s){return function(t){s._trigger(e,t,i._uiHash(i))}}for(this.fromOutside&&!e&&s.push(function(t){this._trigger("receive",t,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||e||s.push(function(t){this._trigger("update",t,this._uiHash())}),this!==this.currentContainer&&(e||(s.push(function(t){this._trigger("remove",t,this._uiHash())}),s.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),s.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),i=this.containers.length-1;0<=i;i--)e||s.push(n("deactivate",this,this.containers[i])),this.containers[i].containerCache.over&&(s.push(n("out",this,this.containers[i])),this.containers[i].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,e||this._trigger("beforeStop",t,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!e){for(i=0;i<s.length;i++)s[i].call(this,t);this._trigger("stop",t,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){!1===V.Widget.prototype._trigger.apply(this,arguments)&&this.cancel()},_uiHash:function(t){var e=t||this;return{helper:e.helper,placeholder:e.placeholder||V([]),position:e.position,originalPosition:e.originalPosition,offset:e.positionAbs,item:e.currentItem,sender:t?t.element:null}}});function ht(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}V.widget("ui.spinner",{version:"1.13.2",defaultElement:"<input>",widgetEventPrefix:"spin",options:{classes:{"ui-spinner":"ui-corner-all","ui-spinner-down":"ui-corner-br","ui-spinner-up":"ui-corner-tr"},culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var s=this._super(),n=this.element;return V.each(["min","max","step"],function(t,e){var i=n.attr(e);null!=i&&i.length&&(s[e]=i)}),s},_events:{keydown:function(t){this._start(t)&&this._keydown(t)&&t.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(t){this.cancelBlur?delete this.cancelBlur:(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",t))},mousewheel:function(t,e){var i=V.ui.safeActiveElement(this.document[0]);if(this.element[0]===i&&e){if(!this.spinning&&!this._start(t))return!1;this._spin((0<e?1:-1)*this.options.step,t),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(t)},100),t.preventDefault()}},"mousedown .ui-spinner-button":function(t){var e;function i(){this.element[0]===V.ui.safeActiveElement(this.document[0])||(this.element.trigger("focus"),this.previous=e,this._delay(function(){this.previous=e}))}e=this.element[0]===V.ui.safeActiveElement(this.document[0])?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),!1!==this._start(t)&&this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){if(V(t.currentTarget).hasClass("ui-state-active"))return!1!==this._start(t)&&void this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseleave .ui-spinner-button":"_stop"},_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap("<span>").parent().append("<a></a><a></a>")},_draw:function(){this._enhance(),this._addClass(this.uiSpinner,"ui-spinner","ui-widget ui-widget-content"),this._addClass("ui-spinner-input"),this.element.attr("role","spinbutton"),this.buttons=this.uiSpinner.children("a").attr("tabIndex",-1).attr("aria-hidden",!0).button({classes:{"ui-button":""}}),this._removeClass(this.buttons,"ui-corner-all"),this._addClass(this.buttons.first(),"ui-spinner-button ui-spinner-up"),this._addClass(this.buttons.last(),"ui-spinner-button ui-spinner-down"),this.buttons.first().button({icon:this.options.icons.up,showLabel:!1}),this.buttons.last().button({icon:this.options.icons.down,showLabel:!1}),this.buttons.height()>Math.ceil(.5*this.uiSpinner.height())&&0<this.uiSpinner.height()&&this.uiSpinner.height(this.uiSpinner.height())},_keydown:function(t){var e=this.options,i=V.ui.keyCode;switch(t.keyCode){case i.UP:return this._repeat(null,1,t),!0;case i.DOWN:return this._repeat(null,-1,t),!0;case i.PAGE_UP:return this._repeat(null,e.page,t),!0;case i.PAGE_DOWN:return this._repeat(null,-e.page,t),!0}return!1},_start:function(t){return!(!this.spinning&&!1===this._trigger("start",t))&&(this.counter||(this.counter=1),this.spinning=!0)},_repeat:function(t,e,i){t=t||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,e,i)},t),this._spin(e*this.options.step,i)},_spin:function(t,e){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+t*this._increment(this.counter)),this.spinning&&!1===this._trigger("spin",e,{value:i})||(this._value(i),this.counter++)},_increment:function(t){var e=this.options.incremental;return e?"function"==typeof e?e(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_adjustValue:function(t){var e=this.options,i=null!==e.min?e.min:0,s=t-i;return t=i+Math.round(s/e.step)*e.step,t=parseFloat(t.toFixed(this._precision())),null!==e.max&&t>e.max?e.max:null!==e.min&&t<e.min?e.min:t},_stop:function(t){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",t))},_setOption:function(t,e){var i;if("culture"===t||"numberFormat"===t)return i=this._parse(this.element.val()),this.options[t]=e,void this.element.val(this._format(i));"max"!==t&&"min"!==t&&"step"!==t||"string"==typeof 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<style type="text/css">

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width: 20%;
max-height: 90%;
overflow: auto;
margin-left: 2%;
position: fixed;
border: 1px solid #ccc;
border-radius: 6px;
}

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list-style: none;
margin: 0;
padding: 0;
border: none;
line-height: 30px;
}

.tocify-header {
text-indent: 10px;
}

.tocify-subheader {
text-indent: 20px;
display: none;
}

.tocify-subheader li {
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}

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text-indent: 60px;
}

.tocify .tocify-item > a, .tocify .nav-list .nav-header {
margin: 0px;
}

.tocify .tocify-item a, .tocify .list-group-item {
padding: 5px;
}
.tocify .nav-pills > li {
float: none;
}


</style>
<script>/* jquery Tocify - v1.9.1 - 2013-10-22
 * http://www.gregfranko.com/jquery.tocify.js/
 * Copyright (c) 2013 Greg Franko; Licensed MIT */

// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic.  I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE.
(function(tocify) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    // Calls the second IIFE and locally passes in the global jQuery, window, and document objects
    tocify(window.jQuery, window, document);

  }

  // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable.  The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set.  All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript.
  (function($, window, document, undefined) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    var tocClassName = "tocify",
      tocClass = "." + tocClassName,
      tocFocusClassName = "tocify-focus",
      tocHoverClassName = "tocify-hover",
      hideTocClassName = "tocify-hide",
      hideTocClass = "." + hideTocClassName,
      headerClassName = "tocify-header",
      headerClass = "." + headerClassName,
      subheaderClassName = "tocify-subheader",
      subheaderClass = "." + subheaderClassName,
      itemClassName = "tocify-item",
      itemClass = "." + itemClassName,
      extendPageClassName = "tocify-extend-page",
      extendPageClass = "." + extendPageClassName;

    // Calling the jQueryUI Widget Factory Method
    $.widget("toc.tocify", {

      //Plugin version
      version: "1.9.1",

      // These options will be used as defaults
      options: {

        // **context**: Accepts String: Any jQuery selector
        // The container element that holds all of the elements used to generate the table of contents
        context: "body",

        // **ignoreSelector**: Accepts String: Any jQuery selector
        // A selector to any element that would be matched by selectors that you wish to be ignored
        ignoreSelector: null,

        // **selectors**: Accepts an Array of Strings: Any jQuery selectors
        // The element's used to generate the table of contents.  The order is very important since it will determine the table of content's nesting structure
        selectors: "h1, h2, h3",

        // **showAndHide**: Accepts a boolean: true or false
        // Used to determine if elements should be shown and hidden
        showAndHide: true,

        // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown"
        // Used to display any of the table of contents nested items
        showEffect: "slideDown",

        // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the show animation
        showEffectSpeed: "medium",

        // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp"
        // Used to hide any of the table of contents nested items
        hideEffect: "slideUp",

        // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the hide animation
        hideEffectSpeed: "medium",

        // **smoothScroll**: Accepts a boolean: true or false
        // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page
        smoothScroll: true,

        // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the smoothScroll animation
        smoothScrollSpeed: "medium",

        // **scrollTo**: Accepts Number (pixels)
        // The amount of space between the top of page and the selected table of contents item after the page has been scrolled
        scrollTo: 0,

        // **showAndHideOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be shown and hidden while scrolling
        showAndHideOnScroll: true,

        // **highlightOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling
        highlightOnScroll: true,

        // **highlightOffset**: Accepts a number
        // The offset distance in pixels to trigger the next active table of contents item
        highlightOffset: 40,

        // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none"
        // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents
        theme: "bootstrap",

        // **extendPage**: Accepts a boolean: true or false
        // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased
        extendPage: true,

        // **extendPageOffset**: Accepts a number: pixels
        // How close to the bottom of the page a user must scroll before the page is extended
        extendPageOffset: 100,

        // **history**: Accepts a boolean: true or false
        // Adds a hash to the page url to maintain history
        history: true,

        // **scrollHistory**: Accepts a boolean: true or false
        // Adds a hash to the page url, to maintain history, when scrolling to a TOC item
        scrollHistory: false,

        // **hashGenerator**: How the hash value (the anchor segment of the URL, following the
        // # character) will be generated.
        //
        // "compact" (default) - #CompressesEverythingTogether
        // "pretty" - #looks-like-a-nice-url-and-is-easily-readable
        // function(text, element){} - Your own hash generation function that accepts the text as an
        // argument, and returns the hash value.
        hashGenerator: "compact",

        // **highlightDefault**: Accepts a boolean: true or false
        // Set's the first TOC item as active if no other TOC item is active.
        highlightDefault: true

      },

      // _Create
      // -------
      //      Constructs the plugin.  Only called once.
      _create: function() {

        var self = this;

        self.extendPageScroll = true;

        // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings)
        self.items = [];

        // Generates the HTML for the dynamic table of contents
        self._generateToc();

        // Adds CSS classes to the newly generated table of contents HTML
        self._addCSSClasses();

        self.webkit = (function() {

          for (var prop in window) {

            if (prop) {

              if (prop.toLowerCase().indexOf("webkit") !== -1) {

                return true;

              }

            }

          }

          return false;

        }());

        // Adds jQuery event handlers to the newly generated table of contents
        self._setEventHandlers();

        // Binding to the Window load event to make sure the correct scrollTop is calculated
        $(window).on("load", function() {

          // Sets the active TOC item
          self._setActiveElement(true);

          // Once all animations on the page are complete, this callback function will be called
          $("html, body").promise().done(function() {

            setTimeout(function() {

              self.extendPageScroll = false;

            }, 0);

          });

        });

      },

      // _generateToc
      // ------------
      //      Generates the HTML for the dynamic table of contents
      _generateToc: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above
          firstElem,

          // Instantiated variable that will store the top level newly created unordered list DOM element
          ul,
          ignoreSelector = self.options.ignoreSelector;


        // Determine the element to start the toc with
        // get all the top level selectors
        firstElem = [];
        var selectors = this.options.selectors.replace(/ /g, "").split(",");
        // find the first set that have at least one non-ignored element
        for(var i = 0; i < selectors.length; i++) {
          var foundSelectors = $(this.options.context).find(selectors[i]);
          for (var s = 0; s < foundSelectors.length; s++) {
            if (!$(foundSelectors[s]).is(ignoreSelector)) {
              firstElem = foundSelectors;
              break;
            }
          }
          if (firstElem.length> 0)
            break;
        }

        if (!firstElem.length) {

          self.element.addClass(hideTocClassName);

          return;

        }

        self.element.addClass(tocClassName);

        // Loops through each top level selector
        firstElem.each(function(index) {

          //If the element matches the ignoreSelector then we skip it
          if ($(this).is(ignoreSelector)) {
            return;
          }

          // Creates an unordered list HTML element and adds a dynamic ID and standard class name
          ul = $("<ul/>", {
            "id": headerClassName + index,
            "class": headerClassName
          }).

          // Appends a top level list item HTML element to the previously created HTML header
          append(self._nestElements($(this), index));

          // Add the created unordered list element to the HTML element calling the plugin
          self.element.append(ul);

          // Finds all of the HTML tags between the header and subheader elements
          $(this).nextUntil(this.nodeName.toLowerCase()).each(function() {

            // If there are no nested subheader elemements
            if ($(this).find(self.options.selectors).length === 0) {

              // Loops through all of the subheader elements
              $(this).filter(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

            // If there are nested subheader elements
            else {

              // Loops through all of the subheader elements
              $(this).find(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

          });

        });

      },

      _setActiveElement: function(pageload) {

        var self = this,

          hash = window.location.hash.substring(1),

          elem = self.element.find('li[data-unique="' + hash + '"]');

        if (hash.length) {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          elem.addClass(self.focusClass);

          // Triggers the click event on the currently focused TOC item
          elem.click();

        } else {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          if (!hash.length && pageload && self.options.highlightDefault) {

            // Highlights the first TOC item if no other items are highlighted
            self.element.find(itemClass).first().addClass(self.focusClass);

          }

        }

        return self;

      },

      // _nestElements
      // -------------
      //      Helps create the table of contents list by appending nested list items
      _nestElements: function(self, index) {

        var arr, item, hashValue;

        arr = $.grep(this.items, function(item) {

          return item === self.text();

        });

        // If there is already a duplicate TOC item
        if (arr.length) {

          // Adds the current TOC item text and index (for slight randomization) to the internal array
          this.items.push(self.text() + index);

        }

        // If there not a duplicate TOC item
        else {

          // Adds the current TOC item text to the internal array
          this.items.push(self.text());

        }

        hashValue = this._generateHashValue(arr, self, index);

        // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
        item = $("<li/>", {

          // Sets a common class name to the list item
          "class": itemClassName,

          "data-unique": hashValue

        });

        if (this.options.theme !== "bootstrap3") {

          item.append($("<a/>", {

            "html": self.html()

          }));

        } else {

          item.html(self.html());

        }

        // Adds an HTML anchor tag before the currently traversed HTML element
        self.before($("<div/>", {

          // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore)
          "name": hashValue,

          "data-unique": hashValue

        }));

        return item;

      },

      // _generateHashValue
      // ------------------
      //      Generates the hash value that will be used to refer to each item.
      _generateHashValue: function(arr, self, index) {

        var hashValue = "",
          hashGeneratorOption = this.options.hashGenerator;

        if (hashGeneratorOption === "pretty") {

          // prettify the text
          hashValue = self.text().toLowerCase().replace(/\s/g, "-");

          // fix double hyphens
          while (hashValue.indexOf("--") > -1) {
            hashValue = hashValue.replace(/--/g, "-");
          }

          // fix colon-space instances
          while (hashValue.indexOf(":-") > -1) {
            hashValue = hashValue.replace(/:-/g, "-");
          }

        } else if (typeof hashGeneratorOption === "function") {

          // call the function
          hashValue = hashGeneratorOption(self.text(), self);

        } else {

          // compact - the default
          hashValue = self.text().replace(/\s/g, "");

        }

        // add the index if we need to
        if (arr.length) {
          hashValue += "" + index;
        }

        // return the value
        return hashValue;

      },

      // _appendElements
      // ---------------
      //      Helps create the table of contents list by appending subheader elements

      _appendSubheaders: function(self, ul) {

        // The current element index
        var index = $(this).index(self.options.selectors),

          // Finds the previous header DOM element
          previousHeader = $(self.options.selectors).eq(index - 1),

          currentTagName = +$(this).prop("tagName").charAt(1),

          previousTagName = +previousHeader.prop("tagName").charAt(1),

          lastSubheader;

        // If the current header DOM element is smaller than the previous header DOM element or the first subheader
        if (currentTagName < previousTagName) {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index));

        }

        // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element
        else if (currentTagName === previousTagName) {

          ul.find(itemClass).last().after(self._nestElements($(this), index));

        } else {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          ul.find(itemClass).last().

          // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name
          after($("<ul/>", {

            "class": subheaderClassName,

            "data-tag": currentTagName

          })).next(subheaderClass).

          // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
          append(self._nestElements($(this), index));
        }

      },

      // _setEventHandlers
      // ----------------
      //      Adds jQuery event handlers to the newly generated table of contents
      _setEventHandlers: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // Instantiates a new variable that will be used to hold a specific element's context
          $self,

          // Instantiates a new variable that will be used to determine the smoothScroll animation time duration
          duration;

        // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin
        this.element.on("click.tocify", "li", function(event) {

          if (self.options.history) {

            window.location.hash = $(this).attr("data-unique");

          }

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          $(this).addClass(self.focusClass);

          // If the showAndHide option is true
          if (self.options.showAndHide) {

            var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]');

            self._triggerShow(elem);

          }

          self._scrollTo($(this));

        });

        // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin
        this.element.find("li").on({

          // Mouseenter event handler
          "mouseenter.tocify": function() {

            // Adds a hover CSS class to the current list item
            $(this).addClass(self.hoverClass);

            // Makes sure the cursor is set to the pointer icon
            $(this).css("cursor", "pointer");

          },

          // Mouseleave event handler
          "mouseleave.tocify": function() {

            if (self.options.theme !== "bootstrap") {

              // Removes the hover CSS class from the current list item
              $(this).removeClass(self.hoverClass);

            }

          }
        });

        // only attach handler if needed (expensive in IE)
        if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) {
          // Window scroll event handler
          $(window).on("scroll.tocify", function() {

            // Once all animations on the page are complete, this callback function will be called
            $("html, body").promise().done(function() {

              // Local variables

              // Stores how far the user has scrolled
              var winScrollTop = $(window).scrollTop(),

                // Stores the height of the window
                winHeight = $(window).height(),

                // Stores the height of the document
                docHeight = $(document).height(),

                scrollHeight = $("body")[0].scrollHeight,

                // Instantiates a variable that will be used to hold a selected HTML element
                elem,

                lastElem,

                lastElemOffset,

                currentElem;

              if (self.options.extendPage) {

                // If the user has scrolled to the bottom of the page and the last toc item is not focused
                if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) {

                  if (!$(extendPageClass).length) {

                    lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]');

                    if (!lastElem.length) return;

                    // Gets the top offset of the page header that is linked to the last toc item
                    lastElemOffset = lastElem.offset().top;

                    // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset
                    $(self.options.context).append($("<div/>", {

                      "class": extendPageClassName,

                      "height": Math.abs(lastElemOffset - winScrollTop) + "px",

                      "data-unique": extendPageClassName

                    }));

                    if (self.extendPageScroll) {

                      currentElem = self.element.find('li.' + self.focusClass);

                      self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]'));

                    }

                  }

                }

              }

              // The zero timeout ensures the following code is run after the scroll events
              setTimeout(function() {

                // _Local variables_

                // Stores the distance to the closest anchor
                var closestAnchorDistance = null,

                  // Stores the index of the closest anchor
                  closestAnchorIdx = null,

                  // Keeps a reference to all anchors
                  anchors = $(self.options.context).find("div[data-unique]"),

                  anchorText;

                // Determines the index of the closest anchor
                anchors.each(function(idx) {
                  var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset);
                  if (closestAnchorDistance == null || distance < closestAnchorDistance) {
                    closestAnchorDistance = distance;
                    closestAnchorIdx = idx;
                  } else {
                    return false;
                  }
                });

                anchorText = $(anchors[closestAnchorIdx]).attr("data-unique");

                // Stores the list item HTML element that corresponds to the currently traversed anchor tag
                elem = $('li[data-unique="' + anchorText + '"]');

                // If the `highlightOnScroll` option is true and a next element is found
                if (self.options.highlightOnScroll && elem.length) {

                  // Removes highlighting from all of the list item's
                  self.element.find("." + self.focusClass).removeClass(self.focusClass);

                  // Highlights the corresponding list item
                  elem.addClass(self.focusClass);

                }

                if (self.options.scrollHistory) {

                  if (window.location.hash !== "#" + anchorText) {

                    window.location.replace("#" + anchorText);

                  }
                }

                // If the `showAndHideOnScroll` option is true
                if (self.options.showAndHideOnScroll && self.options.showAndHide) {

                  self._triggerShow(elem, true);

                }

              }, 0);

            });

          });
        }

      },

      // Show
      // ----
      //      Opens the current sub-header
      show: function(elem, scroll) {

        // Stores the plugin context in the `self` variable
        var self = this,
          element = elem;

        // If the sub-header is not already visible
        if (!elem.is(":visible")) {

          // If the current element does not have any nested subheaders, is not a header, and its parent is not visible
          if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) {

            // Sets the current element to all of the subheaders within the current header
            elem = elem.parents(subheaderClass).add(elem);

          }

          // If the current element does not have any nested subheaders and is not a header
          else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) {

            // Sets the current element to the closest subheader
            elem = elem.closest(subheaderClass);

          }

          //Determines what jQuery effect to use
          switch (self.options.showEffect) {

            //Uses `no effect`
            case "none":

              elem.show();

              break;

              //Uses the jQuery `show` special effect
            case "show":

              elem.show(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `slideDown` special effect
            case "slideDown":

              elem.slideDown(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `fadeIn` special effect
            case "fadeIn":

              elem.fadeIn(self.options.showEffectSpeed);

              break;

              //If none of the above options were passed, then a `jQueryUI show effect` is expected
            default:

              elem.show();

              break;

          }

        }

        // If the current subheader parent element is a header
        if (elem.parent().is(headerClass)) {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem));

        }

        // If the current subheader parent element is not a header
        else {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings())));

        }

        // Maintains chainablity
        return self;

      },

      // Hide
      // ----
      //      Closes the current sub-header
      hide: function(elem) {

        // Stores the plugin context in the `self` variable
        var self = this;

        //Determines what jQuery effect to use
        switch (self.options.hideEffect) {

          // Uses `no effect`
          case "none":

            elem.hide();

            break;

            // Uses the jQuery `hide` special effect
          case "hide":

            elem.hide(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `slideUp` special effect
          case "slideUp":

            elem.slideUp(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `fadeOut` special effect
          case "fadeOut":

            elem.fadeOut(self.options.hideEffectSpeed);

            break;

            // If none of the above options were passed, then a `jqueryUI hide effect` is expected
          default:

            elem.hide();

            break;

        }

        // Maintains chainablity
        return self;
      },

      // _triggerShow
      // ------------
      //      Determines what elements get shown on scroll and click
      _triggerShow: function(elem, scroll) {

        var self = this;

        // If the current element's parent is a header element or the next element is a nested subheader element
        if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) {

          // Shows the next sub-header element
          self.show(elem.next(subheaderClass), scroll);

        }

        // If the current element's parent is a subheader element
        else if (elem.parent().is(subheaderClass)) {

          // Shows the parent sub-header element
          self.show(elem.parent(), scroll);

        }

        // Maintains chainability
        return self;

      },

      // _addCSSClasses
      // --------------
      //      Adds CSS classes to the newly generated table of contents HTML
      _addCSSClasses: function() {

        // If the user wants a jqueryUI theme
        if (this.options.theme === "jqueryui") {

          this.focusClass = "ui-state-default";

          this.hoverClass = "ui-state-hover";

          //Adds the default styling to the dropdown list
          this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content");

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap") {

          this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list");

          this.focusClass = "active";

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap3") {

          this.element.find(headerClass + "," + subheaderClass).addClass("list-group");

          this.element.find(itemClass).addClass("list-group-item");

          this.focusClass = "active";

        }

        // If a user does not want a prebuilt theme
        else {

          // Adds more neutral classes (instead of jqueryui)

          this.focusClass = tocFocusClassName;

          this.hoverClass = tocHoverClassName;

        }

        //Maintains chainability
        return this;

      },

      // setOption
      // ---------
      //      Sets a single Tocify option after the plugin is invoked
      setOption: function() {

        // Calls the jQueryUI Widget Factory setOption method
        $.Widget.prototype._setOption.apply(this, arguments);

      },

      // setOptions
      // ----------
      //      Sets a single or multiple Tocify options after the plugin is invoked
      setOptions: function() {

        // Calls the jQueryUI Widget Factory setOptions method
        $.Widget.prototype._setOptions.apply(this, arguments);

      },

      // _scrollTo
      // ---------
      //      Scrolls to a specific element
      _scrollTo: function(elem) {

        var self = this,
          duration = self.options.smoothScroll || 0,
          scrollTo = self.options.scrollTo,
          currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]');

        if (!currentDiv.length) {

          return self;

        }

        // Once all animations on the page are complete, this callback function will be called
        $("html, body").promise().done(function() {

          // Animates the html and body element scrolltops
          $("html, body").animate({

            // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag
            "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px"

          }, {

            // Sets the smoothScroll animation time duration to the smoothScrollSpeed option
            "duration": duration

          });

        });

        // Maintains chainability
        return self;

      }

    });

  })); //end of plugin
</script>
<script>

/**
 * jQuery Plugin: Sticky Tabs
 *
 * @author Aidan Lister <aidan@php.net>
 * adapted by Ruben Arslan to activate parent tabs too
 * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
 */
(function($) {
  "use strict";
  $.fn.rmarkdownStickyTabs = function() {
    var context = this;
    // Show the tab corresponding with the hash in the URL, or the first tab
    var showStuffFromHash = function() {
      var hash = window.location.hash;
      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
      var $selector = $(selector, context);
      if($selector.data('toggle') === "tab") {
        $selector.tab('show');
        // walk up the ancestors of this element, show any hidden tabs
        $selector.parents('.section.tabset').each(function(i, elm) {
          var link = $('a[href="#' + $(elm).attr('id') + '"]');
          if(link.data('toggle') === "tab") {
            link.tab("show");
          }
        });
      }
    };


    // Set the correct tab when the page loads
    showStuffFromHash(context);

    // Set the correct tab when a user uses their back/forward button
    $(window).on('hashchange', function() {
      showStuffFromHash(context);
    });

    // Change the URL when tabs are clicked
    $('a', context).on('click', function(e) {
      history.pushState(null, null, this.href);
      showStuffFromHash(context);
    });

    return this;
  };
}(jQuery));

window.buildTabsets = function(tocID) {

  // build a tabset from a section div with the .tabset class
  function buildTabset(tabset) {

    // check for fade and pills options
    var fade = tabset.hasClass("tabset-fade");
    var pills = tabset.hasClass("tabset-pills");
    var navClass = pills ? "nav-pills" : "nav-tabs";

    // determine the heading level of the tabset and tabs
    var match = tabset.attr('class').match(/level(\d) /);
    if (match === null)
      return;
    var tabsetLevel = Number(match[1]);
    var tabLevel = tabsetLevel + 1;

    // find all subheadings immediately below
    var tabs = tabset.find("div.section.level" + tabLevel);
    if (!tabs.length)
      return;

    // create tablist and tab-content elements
    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
    $(tabs[0]).before(tabList);
    var tabContent = $('<div class="tab-content"></div>');
    $(tabs[0]).before(tabContent);

    // build the tabset
    var activeTab = 0;
    tabs.each(function(i) {

      // get the tab div
      var tab = $(tabs[i]);

      // get the id then sanitize it for use with bootstrap tabs
      var id = tab.attr('id');

      // see if this is marked as the active tab
      if (tab.hasClass('active'))
        activeTab = i;

      // remove any table of contents entries associated with
      // this ID (since we'll be removing the heading element)
      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();

      // sanitize the id for use with bootstrap tabs
      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
      tab.attr('id', id);

      // get the heading element within it, grab it's text, then remove it
      var heading = tab.find('h' + tabLevel + ':first');
      var headingText = heading.html();
      heading.remove();

      // build and append the tab list item
      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
      a.attr('href', '#' + id);
      a.attr('aria-controls', id);
      var li = $('<li role="presentation"></li>');
      li.append(a);
      tabList.append(li);

      // set it's attributes
      tab.attr('role', 'tabpanel');
      tab.addClass('tab-pane');
      tab.addClass('tabbed-pane');
      if (fade)
        tab.addClass('fade');

      // move it into the tab content div
      tab.detach().appendTo(tabContent);
    });

    // set active tab
    $(tabList.children('li')[activeTab]).addClass('active');
    var active = $(tabContent.children('div.section')[activeTab]);
    active.addClass('active');
    if (fade)
      active.addClass('in');

    if (tabset.hasClass("tabset-sticky"))
      tabset.rmarkdownStickyTabs();
  }

  // convert section divs with the .tabset class to tabsets
  var tabsets = $("div.section.tabset");
  tabsets.each(function(i) {
    buildTabset($(tabsets[i]));
  });
};

</script>
<script>
window.initializeCodeFolding = function(show) {

  // handlers for show-all and hide all
  $("#rmd-show-all-code").click(function() {
    $('div.r-code-collapse').each(function() {
      $(this).collapse('show');
    });
  });
  $("#rmd-hide-all-code").click(function() {
    $('div.r-code-collapse').each(function() {
      $(this).collapse('hide');
    });
  });

  // index for unique code element ids
  var currentIndex = 1;

  // select all R code blocks
  var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable');
  rCodeBlocks.each(function() {
    // skip if the block has fold-none class
    if ($(this).hasClass('fold-none')) return;

    // create a collapsable div to wrap the code in
    var div = $('<div class="collapse r-code-collapse"></div>');
    var showThis = (show || $(this).hasClass('fold-show')) && !$(this).hasClass('fold-hide');
    var id = 'rcode-643E0F36' + currentIndex++;
    div.attr('id', id);
    $(this).before(div);
    $(this).detach().appendTo(div);

    // add a show code button right above
    var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Show') + '</span>');
    var showCodeButton = $('<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm code-folding-btn pull-right float-right"></button>');
    showCodeButton.append(showCodeText);
    showCodeButton
        .attr('data-toggle', 'collapse')
        .attr('data-bs-toggle', 'collapse') // BS5
        .attr('data-target', '#' + id)
        .attr('data-bs-target', '#' + id)   // BS5
        .attr('aria-expanded', showThis)
        .attr('aria-controls', id);

    var buttonRow = $('<div class="row"></div>');
    var buttonCol = $('<div class="col-md-12"></div>');

    buttonCol.append(showCodeButton);
    buttonRow.append(buttonCol);

    div.before(buttonRow);

    // show the div if necessary
    if (showThis) div.collapse('show');

    // update state of button on show/hide
    //   * Change text
    //   * add a class for intermediate states styling
    div.on('hide.bs.collapse', function () {
      showCodeText.text('Show');
      showCodeButton.addClass('btn-collapsing');
    });
    div.on('hidden.bs.collapse', function () {
      showCodeButton.removeClass('btn-collapsing');
    });
    div.on('show.bs.collapse', function () {
      showCodeText.text('Hide');
      showCodeButton.addClass('btn-expanding');
    });
    div.on('shown.bs.collapse', function () {
      showCodeButton.removeClass('btn-expanding');
    });

  });

}
</script>
<script>(function() {
  // If window.HTMLWidgets is already defined, then use it; otherwise create a
  // new object. This allows preceding code to set options that affect the
  // initialization process (though none currently exist).
  window.HTMLWidgets = window.HTMLWidgets || {};

  // See if we're running in a viewer pane. If not, we're in a web browser.
  var viewerMode = window.HTMLWidgets.viewerMode =
      /\bviewer_pane=1\b/.test(window.location);

  // See if we're running in Shiny mode. If not, it's a static document.
  // Note that static widgets can appear in both Shiny and static modes, but
  // obviously, Shiny widgets can only appear in Shiny apps/documents.
  var shinyMode = window.HTMLWidgets.shinyMode =
      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;

  // We can't count on jQuery being available, so we implement our own
  // version if necessary.
  function querySelectorAll(scope, selector) {
    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
      return scope.find(selector);
    }
    if (scope.querySelectorAll) {
      return scope.querySelectorAll(selector);
    }
  }

  function asArray(value) {
    if (value === null)
      return [];
    if ($.isArray(value))
      return value;
    return [value];
  }

  // Implement jQuery's extend
  function extend(target /*, ... */) {
    if (arguments.length == 1) {
      return target;
    }
    for (var i = 1; i < arguments.length; i++) {
      var source = arguments[i];
      for (var prop in source) {
        if (source.hasOwnProperty(prop)) {
          target[prop] = source[prop];
        }
      }
    }
    return target;
  }

  // IE8 doesn't support Array.forEach.
  function forEach(values, callback, thisArg) {
    if (values.forEach) {
      values.forEach(callback, thisArg);
    } else {
      for (var i = 0; i < values.length; i++) {
        callback.call(thisArg, values[i], i, values);
      }
    }
  }

  // Replaces the specified method with the return value of funcSource.
  //
  // Note that funcSource should not BE the new method, it should be a function
  // that RETURNS the new method. funcSource receives a single argument that is
  // the overridden method, it can be called from the new method. The overridden
  // method can be called like a regular function, it has the target permanently
  // bound to it so "this" will work correctly.
  function overrideMethod(target, methodName, funcSource) {
    var superFunc = target[methodName] || function() {};
    var superFuncBound = function() {
      return superFunc.apply(target, arguments);
    };
    target[methodName] = funcSource(superFuncBound);
  }

  // Add a method to delegator that, when invoked, calls
  // delegatee.methodName. If there is no such method on
  // the delegatee, but there was one on delegator before
  // delegateMethod was called, then the original version
  // is invoked instead.
  // For example:
  //
  // var a = {
  //   method1: function() { console.log('a1'); }
  //   method2: function() { console.log('a2'); }
  // };
  // var b = {
  //   method1: function() { console.log('b1'); }
  // };
  // delegateMethod(a, b, "method1");
  // delegateMethod(a, b, "method2");
  // a.method1();
  // a.method2();
  //
  // The output would be "b1", "a2".
  function delegateMethod(delegator, delegatee, methodName) {
    var inherited = delegator[methodName];
    delegator[methodName] = function() {
      var target = delegatee;
      var method = delegatee[methodName];

      // The method doesn't exist on the delegatee. Instead,
      // call the method on the delegator, if it exists.
      if (!method) {
        target = delegator;
        method = inherited;
      }

      if (method) {
        return method.apply(target, arguments);
      }
    };
  }

  // Implement a vague facsimilie of jQuery's data method
  function elementData(el, name, value) {
    if (arguments.length == 2) {
      return el["htmlwidget_data_" + name];
    } else if (arguments.length == 3) {
      el["htmlwidget_data_" + name] = value;
      return el;
    } else {
      throw new Error("Wrong number of arguments for elementData: " +
        arguments.length);
    }
  }

  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
  function escapeRegExp(str) {
    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
  }

  function hasClass(el, className) {
    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
    return re.test(el.className);
  }

  // elements - array (or array-like object) of HTML elements
  // className - class name to test for
  // include - if true, only return elements with given className;
  //   if false, only return elements *without* given className
  function filterByClass(elements, className, include) {
    var results = [];
    for (var i = 0; i < elements.length; i++) {
      if (hasClass(elements[i], className) == include)
        results.push(elements[i]);
    }
    return results;
  }

  function on(obj, eventName, func) {
    if (obj.addEventListener) {
      obj.addEventListener(eventName, func, false);
    } else if (obj.attachEvent) {
      obj.attachEvent(eventName, func);
    }
  }

  function off(obj, eventName, func) {
    if (obj.removeEventListener)
      obj.removeEventListener(eventName, func, false);
    else if (obj.detachEvent) {
      obj.detachEvent(eventName, func);
    }
  }

  // Translate array of values to top/right/bottom/left, as usual with
  // the "padding" CSS property
  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
  function unpackPadding(value) {
    if (typeof(value) === "number")
      value = [value];
    if (value.length === 1) {
      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
    }
    if (value.length === 2) {
      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
    }
    if (value.length === 3) {
      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
    }
    if (value.length === 4) {
      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
    }
  }

  // Convert an unpacked padding object to a CSS value
  function paddingToCss(paddingObj) {
    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
  }

  // Makes a number suitable for CSS
  function px(x) {
    if (typeof(x) === "number")
      return x + "px";
    else
      return x;
  }

  // Retrieves runtime widget sizing information for an element.
  // The return value is either null, or an object with fill, padding,
  // defaultWidth, defaultHeight fields.
  function sizingPolicy(el) {
    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
    if (!sizingEl)
      return null;
    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
    if (viewerMode) {
      return sp.viewer;
    } else {
      return sp.browser;
    }
  }

  // @param tasks Array of strings (or falsy value, in which case no-op).
  //   Each element must be a valid JavaScript expression that yields a
  //   function. Or, can be an array of objects with "code" and "data"
  //   properties; in this case, the "code" property should be a string
  //   of JS that's an expr that yields a function, and "data" should be
  //   an object that will be added as an additional argument when that
  //   function is called.
  // @param target The object that will be "this" for each function
  //   execution.
  // @param args Array of arguments to be passed to the functions. (The
  //   same arguments will be passed to all functions.)
  function evalAndRun(tasks, target, args) {
    if (tasks) {
      forEach(tasks, function(task) {
        var theseArgs = args;
        if (typeof(task) === "object") {
          theseArgs = theseArgs.concat([task.data]);
          task = task.code;
        }
        var taskFunc = tryEval(task);
        if (typeof(taskFunc) !== "function") {
          throw new Error("Task must be a function! Source:\n" + task);
        }
        taskFunc.apply(target, theseArgs);
      });
    }
  }

  // Attempt eval() both with and without enclosing in parentheses.
  // Note that enclosing coerces a function declaration into
  // an expression that eval() can parse
  // (otherwise, a SyntaxError is thrown)
  function tryEval(code) {
    var result = null;
    try {
      result = eval("(" + code + ")");
    } catch(error) {
      if (!(error instanceof SyntaxError)) {
        throw error;
      }
      try {
        result = eval(code);
      } catch(e) {
        if (e instanceof SyntaxError) {
          throw error;
        } else {
          throw e;
        }
      }
    }
    return result;
  }

  function initSizing(el) {
    var sizing = sizingPolicy(el);
    if (!sizing)
      return;

    var cel = document.getElementById("htmlwidget_container");
    if (!cel)
      return;

    if (typeof(sizing.padding) !== "undefined") {
      document.body.style.margin = "0";
      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
    }

    if (sizing.fill) {
      document.body.style.overflow = "hidden";
      document.body.style.width = "100%";
      document.body.style.height = "100%";
      document.documentElement.style.width = "100%";
      document.documentElement.style.height = "100%";
      cel.style.position = "absolute";
      var pad = unpackPadding(sizing.padding);
      cel.style.top = pad.top + "px";
      cel.style.right = pad.right + "px";
      cel.style.bottom = pad.bottom + "px";
      cel.style.left = pad.left + "px";
      el.style.width = "100%";
      el.style.height = "100%";

      return {
        getWidth: function() { return cel.getBoundingClientRect().width; },
        getHeight: function() { return cel.getBoundingClientRect().height; }
      };

    } else {
      el.style.width = px(sizing.width);
      el.style.height = px(sizing.height);

      return {
        getWidth: function() { return cel.getBoundingClientRect().width; },
        getHeight: function() { return cel.getBoundingClientRect().height; }
      };
    }
  }

  // Default implementations for methods
  var defaults = {
    find: function(scope) {
      return querySelectorAll(scope, "." + this.name);
    },
    renderError: function(el, err) {
      var $el = $(el);

      this.clearError(el);

      // Add all these error classes, as Shiny does
      var errClass = "shiny-output-error";
      if (err.type !== null) {
        // use the classes of the error condition as CSS class names
        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
          return errClass + "-" + type;
        }).join(" ");
      }
      errClass = errClass + " htmlwidgets-error";

      // Is el inline or block? If inline or inline-block, just display:none it
      // and add an inline error.
      var display = $el.css("display");
      $el.data("restore-display-mode", display);

      if (display === "inline" || display === "inline-block") {
        $el.hide();
        if (err.message !== "") {
          var errorSpan = $("<span>").addClass(errClass);
          errorSpan.text(err.message);
          $el.after(errorSpan);
        }
      } else if (display === "block") {
        // If block, add an error just after the el, set visibility:none on the
        // el, and position the error to be on top of the el.
        // Mark it with a unique ID and CSS class so we can remove it later.
        $el.css("visibility", "hidden");
        if (err.message !== "") {
          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
            .css("top", el.offsetTop)
            .css("left", el.offsetLeft)
            // setting width can push out the page size, forcing otherwise
            // unnecessary scrollbars to appear and making it impossible for
            // the element to shrink; so use max-width instead
            .css("maxWidth", el.offsetWidth)
            .css("height", el.offsetHeight);
          errorDiv.text(err.message);
          $el.after(errorDiv);

          // Really dumb way to keep the size/position of the error in sync with
          // the parent element as the window is resized or whatever.
          var intId = setInterval(function() {
            if (!errorDiv[0].parentElement) {
              clearInterval(intId);
              return;
            }
            errorDiv
              .css("top", el.offsetTop)
              .css("left", el.offsetLeft)
              .css("maxWidth", el.offsetWidth)
              .css("height", el.offsetHeight);
          }, 500);
        }
      }
    },
    clearError: function(el) {
      var $el = $(el);
      var display = $el.data("restore-display-mode");
      $el.data("restore-display-mode", null);

      if (display === "inline" || display === "inline-block") {
        if (display)
          $el.css("display", display);
        $(el.nextSibling).filter(".htmlwidgets-error").remove();
      } else if (display === "block"){
        $el.css("visibility", "inherit");
        $(el.nextSibling).filter(".htmlwidgets-error").remove();
      }
    },
    sizing: {}
  };

  // Called by widget bindings to register a new type of widget. The definition
  // object can contain the following properties:
  // - name (required) - A string indicating the binding name, which will be
  //   used by default as the CSS classname to look for.
  // - initialize (optional) - A function(el) that will be called once per
  //   widget element; if a value is returned, it will be passed as the third
  //   value to renderValue.
  // - renderValue (required) - A function(el, data, initValue) that will be
  //   called with data. Static contexts will cause this to be called once per
  //   element; Shiny apps will cause this to be called multiple times per
  //   element, as the data changes.
  window.HTMLWidgets.widget = function(definition) {
    if (!definition.name) {
      throw new Error("Widget must have a name");
    }
    if (!definition.type) {
      throw new Error("Widget must have a type");
    }
    // Currently we only support output widgets
    if (definition.type !== "output") {
      throw new Error("Unrecognized widget type '" + definition.type + "'");
    }
    // TODO: Verify that .name is a valid CSS classname

    // Support new-style instance-bound definitions. Old-style class-bound
    // definitions have one widget "object" per widget per type/class of
    // widget; the renderValue and resize methods on such widget objects
    // take el and instance arguments, because the widget object can't
    // store them. New-style instance-bound definitions have one widget
    // object per widget instance; the definition that's passed in doesn't
    // provide renderValue or resize methods at all, just the single method
    //   factory(el, width, height)
    // which returns an object that has renderValue(x) and resize(w, h).
    // This enables a far more natural programming style for the widget
    // author, who can store per-instance state using either OO-style
    // instance fields or functional-style closure variables (I guess this
    // is in contrast to what can only be called C-style pseudo-OO which is
    // what we required before).
    if (definition.factory) {
      definition = createLegacyDefinitionAdapter(definition);
    }

    if (!definition.renderValue) {
      throw new Error("Widget must have a renderValue function");
    }

    // For static rendering (non-Shiny), use a simple widget registration
    // scheme. We also use this scheme for Shiny apps/documents that also
    // contain static widgets.
    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
    // Merge defaults into the definition; don't mutate the original definition.
    var staticBinding = extend({}, defaults, definition);
    overrideMethod(staticBinding, "find", function(superfunc) {
      return function(scope) {
        var results = superfunc(scope);
        // Filter out Shiny outputs, we only want the static kind
        return filterByClass(results, "html-widget-output", false);
      };
    });
    window.HTMLWidgets.widgets.push(staticBinding);

    if (shinyMode) {
      // Shiny is running. Register the definition with an output binding.
      // The definition itself will not be the output binding, instead
      // we will make an output binding object that delegates to the
      // definition. This is because we foolishly used the same method
      // name (renderValue) for htmlwidgets definition and Shiny bindings
      // but they actually have quite different semantics (the Shiny
      // bindings receive data that includes lots of metadata that it
      // strips off before calling htmlwidgets renderValue). We can't
      // just ignore the difference because in some widgets it's helpful
      // to call this.renderValue() from inside of resize(), and if
      // we're not delegating, then that call will go to the Shiny
      // version instead of the htmlwidgets version.

      // Merge defaults with definition, without mutating either.
      var bindingDef = extend({}, defaults, definition);

      // This object will be our actual Shiny binding.
      var shinyBinding = new Shiny.OutputBinding();

      // With a few exceptions, we'll want to simply use the bindingDef's
      // version of methods if they are available, otherwise fall back to
      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
      // methods in the future, and we want them to be overrideable by
      // HTMLWidget binding definitions, then we'll need to add them to this
      // list.
      delegateMethod(shinyBinding, bindingDef, "getId");
      delegateMethod(shinyBinding, bindingDef, "onValueChange");
      delegateMethod(shinyBinding, bindingDef, "onValueError");
      delegateMethod(shinyBinding, bindingDef, "renderError");
      delegateMethod(shinyBinding, bindingDef, "clearError");
      delegateMethod(shinyBinding, bindingDef, "showProgress");

      // The find, renderValue, and resize are handled differently, because we
      // want to actually decorate the behavior of the bindingDef methods.

      shinyBinding.find = function(scope) {
        var results = bindingDef.find(scope);

        // Only return elements that are Shiny outputs, not static ones
        var dynamicResults = results.filter(".html-widget-output");

        // It's possible that whatever caused Shiny to think there might be
        // new dynamic outputs, also caused there to be new static outputs.
        // Since there might be lots of different htmlwidgets bindings, we
        // schedule execution for later--no need to staticRender multiple
        // times.
        if (results.length !== dynamicResults.length)
          scheduleStaticRender();

        return dynamicResults;
      };

      // Wrap renderValue to handle initialization, which unfortunately isn't
      // supported natively by Shiny at the time of this writing.

      shinyBinding.renderValue = function(el, data) {
        Shiny.renderDependencies(data.deps);
        // Resolve strings marked as javascript literals to objects
        if (!(data.evals instanceof Array)) data.evals = [data.evals];
        for (var i = 0; data.evals && i < data.evals.length; i++) {
          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
        }
        if (!bindingDef.renderOnNullValue) {
          if (data.x === null) {
            el.style.visibility = "hidden";
            return;
          } else {
            el.style.visibility = "inherit";
          }
        }
        if (!elementData(el, "initialized")) {
          initSizing(el);

          elementData(el, "initialized", true);
          if (bindingDef.initialize) {
            var rect = el.getBoundingClientRect();
            var result = bindingDef.initialize(el, rect.width, rect.height);
            elementData(el, "init_result", result);
          }
        }
        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
      };

      // Only override resize if bindingDef implements it
      if (bindingDef.resize) {
        shinyBinding.resize = function(el, width, height) {
          // Shiny can call resize before initialize/renderValue have been
          // called, which doesn't make sense for widgets.
          if (elementData(el, "initialized")) {
            bindingDef.resize(el, width, height, elementData(el, "init_result"));
          }
        };
      }

      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
    }
  };

  var scheduleStaticRenderTimerId = null;
  function scheduleStaticRender() {
    if (!scheduleStaticRenderTimerId) {
      scheduleStaticRenderTimerId = setTimeout(function() {
        scheduleStaticRenderTimerId = null;
        window.HTMLWidgets.staticRender();
      }, 1);
    }
  }

  // Render static widgets after the document finishes loading
  // Statically render all elements that are of this widget's class
  window.HTMLWidgets.staticRender = function() {
    var bindings = window.HTMLWidgets.widgets || [];
    forEach(bindings, function(binding) {
      var matches = binding.find(document.documentElement);
      forEach(matches, function(el) {
        var sizeObj = initSizing(el, binding);

        var getSize = function(el) {
          if (sizeObj) {
            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
          } else {
            var rect = el.getBoundingClientRect();
            return {w: rect.width, h: rect.height}
          }
        };

        if (hasClass(el, "html-widget-static-bound"))
          return;
        el.className = el.className + " html-widget-static-bound";

        var initResult;
        if (binding.initialize) {
          var size = getSize(el);
          initResult = binding.initialize(el, size.w, size.h);
          elementData(el, "init_result", initResult);
        }

        if (binding.resize) {
          var lastSize = getSize(el);
          var resizeHandler = function(e) {
            var size = getSize(el);
            if (size.w === 0 && size.h === 0)
              return;
            if (size.w === lastSize.w && size.h === lastSize.h)
              return;
            lastSize = size;
            binding.resize(el, size.w, size.h, initResult);
          };

          on(window, "resize", resizeHandler);

          // This is needed for cases where we're running in a Shiny
          // app, but the widget itself is not a Shiny output, but
          // rather a simple static widget. One example of this is
          // an rmarkdown document that has runtime:shiny and widget
          // that isn't in a render function. Shiny only knows to
          // call resize handlers for Shiny outputs, not for static
          // widgets, so we do it ourselves.
          if (window.jQuery) {
            window.jQuery(document).on(
              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
              resizeHandler
            );
            window.jQuery(document).on(
              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
              resizeHandler
            );
          }

          // This is needed for the specific case of ioslides, which
          // flips slides between display:none and display:block.
          // Ideally we would not have to have ioslide-specific code
          // here, but rather have ioslides raise a generic event,
          // but the rmarkdown package just went to CRAN so the
          // window to getting that fixed may be long.
          if (window.addEventListener) {
            // It's OK to limit this to window.addEventListener
            // browsers because ioslides itself only supports
            // such browsers.
            on(document, "slideenter", resizeHandler);
            on(document, "slideleave", resizeHandler);
          }
        }

        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
        if (scriptData) {
          var data = JSON.parse(scriptData.textContent || scriptData.text);
          // Resolve strings marked as javascript literals to objects
          if (!(data.evals instanceof Array)) data.evals = [data.evals];
          for (var k = 0; data.evals && k < data.evals.length; k++) {
            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
          }
          binding.renderValue(el, data.x, initResult);
          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
        }
      });
    });

    invokePostRenderHandlers();
  }


  function has_jQuery3() {
    if (!window.jQuery) {
      return false;
    }
    var $version = window.jQuery.fn.jquery;
    var $major_version = parseInt($version.split(".")[0]);
    return $major_version >= 3;
  }

  /*
  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
  / really means $(setTimeout(fn)).
  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
  /
  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
  / one tick later than it did before, which means staticRender() is
  / called renderValue() earlier than (advanced) widget authors might be expecting.
  / https://github.com/rstudio/shiny/issues/2630
  /
  / For a concrete example, leaflet has some methods (e.g., updateBounds)
  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
  / delay execution of those methods (until Shiny methods are ready)
  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
  /
  / Ideally widget authors wouldn't need to use this setTimeout() hack that
  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
  / the logic initShiny should be broken up so that method registration happens
  / right away, but binding happens later.
  */
  function maybeStaticRenderLater() {
    if (shinyMode && has_jQuery3()) {
      window.jQuery(window.HTMLWidgets.staticRender);
    } else {
      window.HTMLWidgets.staticRender();
    }
  }

  if (document.addEventListener) {
    document.addEventListener("DOMContentLoaded", function() {
      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
      maybeStaticRenderLater();
    }, false);
  } else if (document.attachEvent) {
    document.attachEvent("onreadystatechange", function() {
      if (document.readyState === "complete") {
        document.detachEvent("onreadystatechange", arguments.callee);
        maybeStaticRenderLater();
      }
    });
  }


  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
    // If no key, default to the first item
    if (typeof(key) === "undefined")
      key = 1;

    var link = document.getElementById(depname + "-" + key + "-attachment");
    if (!link) {
      throw new Error("Attachment " + depname + "/" + key + " not found in document");
    }
    return link.getAttribute("href");
  };

  window.HTMLWidgets.dataframeToD3 = function(df) {
    var names = [];
    var length;
    for (var name in df) {
        if (df.hasOwnProperty(name))
            names.push(name);
        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
            throw new Error("All fields must be arrays");
        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
            throw new Error("All fields must be arrays of the same length");
        }
        length = df[name].length;
    }
    var results = [];
    var item;
    for (var row = 0; row < length; row++) {
        item = {};
        for (var col = 0; col < names.length; col++) {
            item[names[col]] = df[names[col]][row];
        }
        results.push(item);
    }
    return results;
  };

  window.HTMLWidgets.transposeArray2D = function(array) {
      if (array.length === 0) return array;
      var newArray = array[0].map(function(col, i) {
          return array.map(function(row) {
              return row[i]
          })
      });
      return newArray;
  };
  // Split value at splitChar, but allow splitChar to be escaped
  // using escapeChar. Any other characters escaped by escapeChar
  // will be included as usual (including escapeChar itself).
  function splitWithEscape(value, splitChar, escapeChar) {
    var results = [];
    var escapeMode = false;
    var currentResult = "";
    for (var pos = 0; pos < value.length; pos++) {
      if (!escapeMode) {
        if (value[pos] === splitChar) {
          results.push(currentResult);
          currentResult = "";
        } else if (value[pos] === escapeChar) {
          escapeMode = true;
        } else {
          currentResult += value[pos];
        }
      } else {
        currentResult += value[pos];
        escapeMode = false;
      }
    }
    if (currentResult !== "") {
      results.push(currentResult);
    }
    return results;
  }
  // Function authored by Yihui/JJ Allaire
  window.HTMLWidgets.evaluateStringMember = function(o, member) {
    var parts = splitWithEscape(member, '.', '\\');
    for (var i = 0, l = parts.length; i < l; i++) {
      var part = parts[i];
      // part may be a character or 'numeric' member name
      if (o !== null && typeof o === "object" && part in o) {
        if (i == (l - 1)) { // if we are at the end of the line then evalulate
          if (typeof o[part] === "string")
            o[part] = tryEval(o[part]);
        } else { // otherwise continue to next embedded object
          o = o[part];
        }
      }
    }
  };

  // Retrieve the HTMLWidget instance (i.e. the return value of an
  // HTMLWidget binding's initialize() or factory() function)
  // associated with an element, or null if none.
  window.HTMLWidgets.getInstance = function(el) {
    return elementData(el, "init_result");
  };

  // Finds the first element in the scope that matches the selector,
  // and returns the HTMLWidget instance (i.e. the return value of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with that element, if any. If no element matches the
  // selector, or the first matching element has no HTMLWidget
  // instance associated with it, then null is returned.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.find = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var el = scope.querySelector(selector);
    if (el === null) {
      return null;
    } else {
      return window.HTMLWidgets.getInstance(el);
    }
  };

  // Finds all elements in the scope that match the selector, and
  // returns the HTMLWidget instances (i.e. the return values of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with the elements, in an array. If elements that
  // match the selector don't have an associated HTMLWidget
  // instance, the returned array will contain nulls.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.findAll = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var nodes = scope.querySelectorAll(selector);
    var results = [];
    for (var i = 0; i < nodes.length; i++) {
      results.push(window.HTMLWidgets.getInstance(nodes[i]));
    }
    return results;
  };

  var postRenderHandlers = [];
  function invokePostRenderHandlers() {
    while (postRenderHandlers.length) {
      var handler = postRenderHandlers.shift();
      if (handler) {
        handler();
      }
    }
  }

  // Register the given callback function to be invoked after the
  // next time static widgets are rendered.
  window.HTMLWidgets.addPostRenderHandler = function(callback) {
    postRenderHandlers.push(callback);
  };

  // Takes a new-style instance-bound definition, and returns an
  // old-style class-bound definition. This saves us from having
  // to rewrite all the logic in this file to accomodate both
  // types of definitions.
  function createLegacyDefinitionAdapter(defn) {
    var result = {
      name: defn.name,
      type: defn.type,
      initialize: function(el, width, height) {
        return defn.factory(el, width, height);
      },
      renderValue: function(el, x, instance) {
        return instance.renderValue(x);
      },
      resize: function(el, width, height, instance) {
        return instance.resize(width, height);
      }
    };

    if (defn.find)
      result.find = defn.find;
    if (defn.renderError)
      result.renderError = defn.renderError;
    if (defn.clearError)
      result.clearError = defn.clearError;

    return result;
  }
})();
</script>
<style type="text/css">.dt-crosstalk-fade {
opacity: 0.2;
}
html body div.DTS div.dataTables_scrollBody {
background: none;
}

table.dataTable {
display: table;
}

.html-fill-container > .html-fill-item.datatables {
flex-basis: 400px;
}
</style>
<script>(function() {

// some helper functions: using a global object DTWidget so that it can be used
// in JS() code, e.g. datatable(options = list(foo = JS('code'))); unlike R's
// dynamic scoping, when 'code' is eval()'ed, JavaScript does not know objects
// from the "parent frame", e.g. JS('DTWidget') will not work unless it was made
// a global object
var DTWidget = {};

// 123456666.7890 -> 123,456,666.7890
var markInterval = function(d, digits, interval, mark, decMark, precision) {
  x = precision ? d.toPrecision(digits) : d.toFixed(digits);
  if (!/^-?[\d.]+$/.test(x)) return x;
  var xv = x.split('.');
  if (xv.length > 2) return x;  // should have at most one decimal point
  xv[0] = xv[0].replace(new RegExp('\\B(?=(\\d{' + interval + '})+(?!\\d))', 'g'), mark);
  return xv.join(decMark);
};

DTWidget.formatCurrency = function(data, currency, digits, interval, mark, decMark, before, zeroPrint) {
  var d = parseFloat(data);
  if (isNaN(d)) return '';
  if (zeroPrint !== null && d === 0.0) return zeroPrint;
  var res = markInterval(d, digits, interval, mark, decMark);
  res = before ? (/^-/.test(res) ? '-' + currency + res.replace(/^-/, '') : currency + res) :
    res + currency;
  return res;
};

DTWidget.formatString = function(data, prefix, suffix) {
  var d = data;
  if (d === null) return '';
  return prefix + d + suffix;
};

DTWidget.formatPercentage = function(data, digits, interval, mark, decMark, zeroPrint) {
  var d = parseFloat(data);
  if (isNaN(d)) return '';
  if (zeroPrint !== null && d === 0.0) return zeroPrint;
  return markInterval(d * 100, digits, interval, mark, decMark) + '%';
};

DTWidget.formatRound = function(data, digits, interval, mark, decMark, zeroPrint) {
  var d = parseFloat(data);
  if (isNaN(d)) return '';
  if (zeroPrint !== null && d === 0.0) return zeroPrint;
  return markInterval(d, digits, interval, mark, decMark);
};

DTWidget.formatSignif = function(data, digits, interval, mark, decMark, zeroPrint) {
  var d = parseFloat(data);
  if (isNaN(d)) return '';
  if (zeroPrint !== null && d === 0.0) return zeroPrint;
  return markInterval(d, digits, interval, mark, decMark, true);
};

DTWidget.formatDate = function(data, method, params) {
  var d = data;
  if (d === null) return '';
  // (new Date('2015-10-28')).toDateString() may return 2015-10-27 because the
  // actual time created could be like 'Tue Oct 27 2015 19:00:00 GMT-0500 (CDT)',
  // i.e. the date-only string is treated as UTC time instead of local time
  if ((method === 'toDateString' || method === 'toLocaleDateString') && /^\d{4,}\D\d{2}\D\d{2}$/.test(d)) {
    d = d.split(/\D/);
    d = new Date(d[0], d[1] - 1, d[2]);
  } else {
    d = new Date(d);
  }
  return d[method].apply(d, params);
};

window.DTWidget = DTWidget;

// A helper function to update the properties of existing filters
var setFilterProps = function(td, props) {
  // Update enabled/disabled state
  var $input = $(td).find('input').first();
  var searchable = $input.data('searchable');
  $input.prop('disabled', !searchable || props.disabled);

  // Based on the filter type, set its new values
  var type = td.getAttribute('data-type');
  if (['factor', 'logical'].includes(type)) {
    // Reformat the new dropdown options for use with selectize
    var new_vals = props.params.options.map(function(item) {
      return { text: item, value: item };
    });

    // Find the selectize object
    var dropdown = $(td).find('.selectized').eq(0)[0].selectize;

    // Note the current values
    var old_vals = dropdown.getValue();

    // Remove the existing values
    dropdown.clearOptions();

    // Add the new options
    dropdown.addOption(new_vals);

    // Preserve the existing values
    dropdown.setValue(old_vals);

  } else if (['number', 'integer', 'date', 'time'].includes(type)) {
    // Apply internal scaling to new limits. Updating scale not yet implemented.
    var slider = $(td).find('.noUi-target').eq(0);
    var scale = Math.pow(10, Math.max(0, +slider.data('scale') || 0));
    var new_vals = [props.params.min * scale, props.params.max * scale];

    // Note what the new limits will be just for this filter
    var new_lims = new_vals.slice();

    // Determine the current values and limits
    var old_vals = slider.val().map(Number);
    var old_lims = slider.noUiSlider('options').range;
    old_lims = [old_lims.min, old_lims.max];

    // Preserve the current values if filters have been applied; otherwise, apply no filtering
    if (old_vals[0] != old_lims[0]) {
      new_vals[0] = Math.max(old_vals[0], new_vals[0]);
    }

    if (old_vals[1] != old_lims[1]) {
      new_vals[1] = Math.min(old_vals[1], new_vals[1]);
    }

    // Update the endpoints of the slider
    slider.noUiSlider({
      start: new_vals,
      range: {'min': new_lims[0], 'max': new_lims[1]}
    }, true);
  }
};

var transposeArray2D = function(a) {
  return a.length === 0 ? a : HTMLWidgets.transposeArray2D(a);
};

var crosstalkPluginsInstalled = false;

function maybeInstallCrosstalkPlugins() {
  if (crosstalkPluginsInstalled)
    return;
  crosstalkPluginsInstalled = true;

  $.fn.dataTable.ext.afnFiltering.push(
    function(oSettings, aData, iDataIndex) {
      var ctfilter = oSettings.nTable.ctfilter;
      if (ctfilter && !ctfilter[iDataIndex])
        return false;

      var ctselect = oSettings.nTable.ctselect;
      if (ctselect && !ctselect[iDataIndex])
        return false;

      return true;
    }
  );
}

HTMLWidgets.widget({
  name: "datatables",
  type: "output",
  renderOnNullValue: true,
  initialize: function(el, width, height) {
    // in order that the type=number inputs return a number
    $.valHooks.number = {
      get: function(el) {
        var value = parseFloat(el.value);
        return isNaN(value) ? "" : value;
      }
    };
    $(el).html('&nbsp;');
    return {
      data: null,
      ctfilterHandle: new crosstalk.FilterHandle(),
      ctfilterSubscription: null,
      ctselectHandle: new crosstalk.SelectionHandle(),
      ctselectSubscription: null
    };
  },
  renderValue: function(el, data, instance) {
    if (el.offsetWidth === 0 || el.offsetHeight === 0) {
      instance.data = data;
      return;
    }
    instance.data = null;
    var $el = $(el);
    $el.empty();

    if (data === null) {
      $el.append('&nbsp;');
      // clear previous Shiny inputs (if any)
      for (var i in instance.clearInputs) instance.clearInputs[i]();
      instance.clearInputs = {};
      return;
    }

    var crosstalkOptions = data.crosstalkOptions;
    if (!crosstalkOptions) crosstalkOptions = {
      'key': null, 'group': null
    };
    if (crosstalkOptions.group) {
      maybeInstallCrosstalkPlugins();
      instance.ctfilterHandle.setGroup(crosstalkOptions.group);
      instance.ctselectHandle.setGroup(crosstalkOptions.group);
    }

    // if we are in the viewer then we always want to fillContainer and
    // and autoHideNavigation (unless the user has explicitly set these)
    if (window.HTMLWidgets.viewerMode) {
      if (!data.hasOwnProperty("fillContainer"))
        data.fillContainer = true;
      if (!data.hasOwnProperty("autoHideNavigation"))
        data.autoHideNavigation = true;
    }

    // propagate fillContainer to instance (so we have it in resize)
    instance.fillContainer = data.fillContainer;

    var cells = data.data;

    if (cells instanceof Array) cells = transposeArray2D(cells);

    $el.append(data.container);
    var $table = $el.find('table');
    if (data.class) $table.addClass(data.class);
    if (data.caption) $table.prepend(data.caption);

    if (!data.selection) data.selection = {
      mode: 'none', selected: null, target: 'row', selectable: null
    };
    if (HTMLWidgets.shinyMode && data.selection.mode !== 'none' &&
        data.selection.target === 'row+column') {
      if ($table.children('tfoot').length === 0) {
        $table.append($('<tfoot>'));
        $table.find('thead tr').clone().appendTo($table.find('tfoot'));
      }
    }

    // column filters
    var filterRow;
    switch (data.filter) {
      case 'top':
        $table.children('thead').append(data.filterHTML);
        filterRow = $table.find('thead tr:last td');
        break;
      case 'bottom':
        if ($table.children('tfoot').length === 0) {
          $table.append($('<tfoot>'));
        }
        $table.children('tfoot').prepend(data.filterHTML);
        filterRow = $table.find('tfoot tr:first td');
        break;
    }

    var options = { searchDelay: 1000 };
    if (cells !== null) $.extend(options, {
      data: cells
    });

    // options for fillContainer
    var bootstrapActive = typeof($.fn.popover) != 'undefined';
    if (instance.fillContainer) {

      // force scrollX/scrollY and turn off autoWidth
      options.scrollX = true;
      options.scrollY = "100px"; // can be any value, we'll adjust below

      // if we aren't paginating then move around the info/filter controls
      // to save space at the bottom and rephrase the info callback
      if (data.options.paging === false) {

        // we know how to do this cleanly for bootstrap, not so much
        // for other themes/layouts
        if (bootstrapActive) {
          options.dom = "<'row'<'col-sm-4'i><'col-sm-8'f>>" +
                        "<'row'<'col-sm-12'tr>>";
        }

        options.fnInfoCallback = function(oSettings, iStart, iEnd,
                                           iMax, iTotal, sPre) {
          return Number(iTotal).toLocaleString() + " records";
        };
      }
    }

    // auto hide navigation if requested
    // Note, this only works on client-side processing mode as on server-side,
    // cells (data.data) is null; In addition, we require the pageLength option
    // being provided explicitly to enable this. Despite we may be able to deduce
    // the default value of pageLength, it may complicate things so we'd rather
    // put this responsiblity to users and warn them on the R side.
    if (data.autoHideNavigation === true && data.options.paging !== false) {
      // strip all nav if length >= cells
      if ((cells instanceof Array) && data.options.pageLength >= cells.length)
        options.dom = bootstrapActive ? "<'row'<'col-sm-12'tr>>" : "t";
      // alternatively lean things out for flexdashboard mobile portrait
      else if (bootstrapActive && window.FlexDashboard && window.FlexDashboard.isMobilePhone())
        options.dom = "<'row'<'col-sm-12'f>>" +
                      "<'row'<'col-sm-12'tr>>"  +
                      "<'row'<'col-sm-12'p>>";
    }

    $.extend(true, options, data.options || {});

    var searchCols = options.searchCols;
    if (searchCols) {
      searchCols = searchCols.map(function(x) {
        return x === null ? '' : x.search;
      });
      // FIXME: this means I don't respect the escapeRegex setting
      delete options.searchCols;
    }

    // server-side processing?
    var server = options.serverSide === true;

    // use the dataSrc function to pre-process JSON data returned from R
    var DT_rows_all = [], DT_rows_current = [];
    if (server && HTMLWidgets.shinyMode && typeof options.ajax === 'object' &&
        /^session\/[\da-z]+\/dataobj/.test(options.ajax.url) && !options.ajax.dataSrc) {
      options.ajax.dataSrc = function(json) {
        DT_rows_all = $.makeArray(json.DT_rows_all);
        DT_rows_current = $.makeArray(json.DT_rows_current);
        var data = json.data;
        if (!colReorderEnabled()) return data;
        var table = $table.DataTable(), order = table.colReorder.order(), flag = true, i, j, row;
        for (i = 0; i < order.length; ++i) if (order[i] !== i) flag = false;
        if (flag) return data;
        for (i = 0; i < data.length; ++i) {
          row = data[i].slice();
          for (j = 0; j < order.length; ++j) data[i][j] = row[order[j]];
        }
        return data;
      };
    }

    var thiz = this;
    if (instance.fillContainer) $table.on('init.dt', function(e) {
      thiz.fillAvailableHeight(el, $(el).innerHeight());
    });
    // If the page contains serveral datatables and one of which enables colReorder,
    // the table.colReorder.order() function will exist but throws error when called.
    // So it seems like the only way to know if colReorder is enabled or not is to
    // check the options.
    var colReorderEnabled = function() { return "colReorder" in options; };
    var table = $table.DataTable(options);
    $el.data('datatable', table);

    // Unregister previous Crosstalk event subscriptions, if they exist
    if (instance.ctfilterSubscription) {
      instance.ctfilterHandle.off("change", instance.ctfilterSubscription);
      instance.ctfilterSubscription = null;
    }
    if (instance.ctselectSubscription) {
      instance.ctselectHandle.off("change", instance.ctselectSubscription);
      instance.ctselectSubscription = null;
    }

    if (!crosstalkOptions.group) {
      $table[0].ctfilter = null;
      $table[0].ctselect = null;
    } else {
      var key = crosstalkOptions.key;
      function keysToMatches(keys) {
        if (!keys) {
          return null;
        } else {
          var selectedKeys = {};
          for (var i = 0; i < keys.length; i++) {
            selectedKeys[keys[i]] = true;
          }
          var matches = {};
          for (var j = 0; j < key.length; j++) {
            if (selectedKeys[key[j]])
              matches[j] = true;
          }
          return matches;
        }
      }

      function applyCrosstalkFilter(e) {
        $table[0].ctfilter = keysToMatches(e.value);
        table.draw();
      }
      instance.ctfilterSubscription = instance.ctfilterHandle.on("change", applyCrosstalkFilter);
      applyCrosstalkFilter({value: instance.ctfilterHandle.filteredKeys});

      function applyCrosstalkSelection(e) {
        if (e.sender !== instance.ctselectHandle) {
          table
            .rows('.' + selClass, {search: 'applied'})
            .nodes()
            .to$()
            .removeClass(selClass);
          if (selectedRows)
            changeInput('rows_selected', selectedRows(), void 0, true);
        }

        if (e.sender !== instance.ctselectHandle && e.value && e.value.length) {
          var matches = keysToMatches(e.value);

          // persistent selection with plotly (& leaflet)
          var ctOpts = crosstalk.var("plotlyCrosstalkOpts").get() || {};
          if (ctOpts.persistent === true) {
            var matches = $.extend(matches, $table[0].ctselect);
          }

          $table[0].ctselect = matches;
          table.draw();
        } else {
          if ($table[0].ctselect) {
            $table[0].ctselect = null;
            table.draw();
          }
        }
      }
      instance.ctselectSubscription = instance.ctselectHandle.on("change", applyCrosstalkSelection);
      // TODO: This next line doesn't seem to work when renderDataTable is used
      applyCrosstalkSelection({value: instance.ctselectHandle.value});
    }

    var inArray = function(val, array) {
      return $.inArray(val, $.makeArray(array)) > -1;
    };

    // search the i-th column
    var searchColumn = function(i, value) {
      var regex = false, ci = true;
      if (options.search) {
        regex = options.search.regex,
        ci = options.search.caseInsensitive !== false;
      }
      return table.column(i).search(value, regex, !regex, ci);
    };

    if (data.filter !== 'none') {

      filterRow.each(function(i, td) {

        var $td = $(td), type = $td.data('type'), filter;
        var $input = $td.children('div').first().children('input');
        var disabled = $input.prop('disabled');
        var searchable = table.settings()[0].aoColumns[i].bSearchable;
        $input.prop('disabled', !searchable || disabled);
        $input.data('searchable', searchable); // for updating later
        $input.on('input blur', function() {
          $input.next('span').toggle(Boolean($input.val()));
        });
        // Bootstrap sets pointer-events to none and we won't be able to click
        // the clear button
        $input.next('span').css('pointer-events', 'auto').hide().click(function() {
          $(this).hide().prev('input').val('').trigger('input').focus();
        });
        var searchCol;  // search string for this column
        if (searchCols && searchCols[i]) {
          searchCol = searchCols[i];
          $input.val(searchCol).trigger('input');
        }
        var $x = $td.children('div').last();

        // remove the overflow: hidden attribute of the scrollHead
        // (otherwise the scrolling table body obscures the filters)
        // The workaround and the discussion from
        // https://github.com/rstudio/DT/issues/554#issuecomment-518007347
        // Otherwise the filter selection will not be anchored to the values
        // when the columns number is many and scrollX is enabled.
        var scrollHead = $(el).find('.dataTables_scrollHead,.dataTables_scrollFoot');
        var cssOverflowHead = scrollHead.css('overflow');
        var scrollBody = $(el).find('.dataTables_scrollBody');
        var cssOverflowBody = scrollBody.css('overflow');
        var scrollTable = $(el).find('.dataTables_scroll');
        var cssOverflowTable = scrollTable.css('overflow');
        if (cssOverflowHead === 'hidden') {
          $x.on('show hide', function(e) {
            if (e.type === 'show') {
              scrollHead.css('overflow', 'visible');
              scrollBody.css('overflow', 'visible');
              scrollTable.css('overflow-x', 'scroll');
            } else {
              scrollHead.css('overflow', cssOverflowHead);
              scrollBody.css('overflow', cssOverflowBody);
              scrollTable.css('overflow-x', cssOverflowTable);
            }
          });
          $x.css('z-index', 25);
        }

        if (inArray(type, ['factor', 'logical'])) {
          $input.on({
            click: function() {
              $input.parent().hide(); $x.show().trigger('show'); filter[0].selectize.focus();
            },
            input: function() {
              if ($input.val() === '') filter[0].selectize.setValue([]);
            }
          });
          var $input2 = $x.children('select');
          filter = $input2.selectize({
            options: $input2.data('options').map(function(v, i) {
              return ({text: v, value: v});
            }),
            plugins: ['remove_button'],
            hideSelected: true,
            onChange: function(value) {
              if (value === null) value = []; // compatibility with jQuery 3.0
              $input.val(value.length ? JSON.stringify(value) : '');
              if (value.length) $input.trigger('input');
              $input.attr('title', $input.val());
              if (server) {
                table.column(i).search(value.length ? JSON.stringify(value) : '').draw();
                return;
              }
              // turn off filter if nothing selected
              $td.data('filter', value.length > 0);
              table.draw();  // redraw table, and filters will be applied
            }
          });
          if (searchCol) filter[0].selectize.setValue(JSON.parse(searchCol));
          filter[0].selectize.on('blur', function() {
            $x.hide().trigger('hide'); $input.parent().show(); $input.trigger('blur');
          });
          filter.next('div').css('margin-bottom', 'auto');
        } else if (type === 'character') {
          var fun = function() {
            searchColumn(i, $input.val()).draw();
          };
          if (server) {
            fun = $.fn.dataTable.util.throttle(fun, options.searchDelay);
          }
          $input.on('input', fun);
        } else if (inArray(type, ['number', 'integer', 'date', 'time'])) {
          var $x0 = $x;
          $x = $x0.children('div').first();
          $x0.css({
            'background-color': '#fff',
            'border': '1px #ddd solid',
            'border-radius': '4px',
            'padding': data.vertical ? '35px 20px': '20px 20px 10px 20px'
          });
          var $spans = $x0.children('span').css({
            'margin-top': data.vertical ? '0' : '10px',
            'white-space': 'nowrap'
          });
          var $span1 = $spans.first(), $span2 = $spans.last();
          var r1 = +$x.data('min'), r2 = +$x.data('max');
          // when the numbers are too small or have many decimal places, the
          // slider may have numeric precision problems (#150)
          var scale = Math.pow(10, Math.max(0, +$x.data('scale') || 0));
          r1 = Math.round(r1 * scale); r2 = Math.round(r2 * scale);
          var scaleBack = function(x, scale) {
            if (scale === 1) return x;
            var d = Math.round(Math.log(scale) / Math.log(10));
            // to avoid problems like 3.423/100 -> 0.034230000000000003
            return (x / scale).toFixed(d);
          };
          var slider_min = function() {
            return filter.noUiSlider('options').range.min;
          };
          var slider_max = function() {
            return filter.noUiSlider('options').range.max;
          };
          $input.on({
            focus: function() {
              $x0.show().trigger('show');
              // first, make sure the slider div leaves at least 20px between
              // the two (slider value) span's
              $x0.width(Math.max(160, $span1.outerWidth() + $span2.outerWidth() + 20));
              // then, if the input is really wide or slider is vertical,
              // make the slider the same width as the input
              if ($x0.outerWidth() < $input.outerWidth() || data.vertical) {
                $x0.outerWidth($input.outerWidth());
              }
              // make sure the slider div does not reach beyond the right margin
              if ($(window).width() < $x0.offset().left + $x0.width()) {
                $x0.offset({
                  'left': $input.offset().left + $input.outerWidth() - $x0.outerWidth()
                });
              }
            },
            blur: function() {
              $x0.hide().trigger('hide');
            },
            input: function() {
              if ($input.val() === '') filter.val([slider_min(), slider_max()]);
            },
            change: function() {
              var v = $input.val().replace(/\s/g, '');
              if (v === '') return;
              v = v.split('...');
              if (v.length !== 2) {
                $input.parent().addClass('has-error');
                return;
              }
              if (v[0] === '') v[0] = slider_min();
              if (v[1] === '') v[1] = slider_max();
              $input.parent().removeClass('has-error');
              // treat date as UTC time at midnight
              var strTime = function(x) {
                var s = type === 'date' ? 'T00:00:00Z' : '';
                var t = new Date(x + s).getTime();
                // add 10 minutes to date since it does not hurt the date, and
                // it helps avoid the tricky floating point arithmetic problems,
                // e.g. sometimes the date may be a few milliseconds earlier
                // than the midnight due to precision problems in noUiSlider
                return type === 'date' ? t + 3600000 : t;
              };
              if (inArray(type, ['date', 'time'])) {
                v[0] = strTime(v[0]);
                v[1] = strTime(v[1]);
              }
              if (v[0] != slider_min()) v[0] *= scale;
              if (v[1] != slider_max()) v[1] *= scale;
              filter.val(v);
            }
          });
          var formatDate = function(d, isoFmt) {
            d = scaleBack(d, scale);
            if (type === 'number') return d;
            if (type === 'integer') return parseInt(d);
            var x = new Date(+d);
            var fmt = ('filterDateFmt' in data) ? data.filterDateFmt[i] : undefined;
            if (fmt !== undefined && isoFmt === false) return x[fmt.method].apply(x, fmt.params);
            if (type === 'date') {
              var pad0 = function(x) {
                return ('0' + x).substr(-2, 2);
              };
              return x.getUTCFullYear() + '-' + pad0(1 + x.getUTCMonth())
                      + '-' + pad0(x.getUTCDate());
            } else {
              return x.toISOString();
            }
          };
          var opts = type === 'date' ? { step: 60 * 60 * 1000 } :
                     type === 'integer' ? { step: 1 } : {};

          opts.orientation = data.vertical ? 'vertical': 'horizontal';
          opts.direction = data.vertical ? 'rtl': 'ltr';

          filter = $x.noUiSlider($.extend({
            start: [r1, r2],
            range: {min: r1, max: r2},
            connect: true
          }, opts));
          if (scale > 1) (function() {
            var t1 = r1, t2 = r2;
            var val = filter.val();
            while (val[0] > r1 || val[1] < r2) {
              if (val[0] > r1) {
                t1 -= val[0] - r1;
              }
              if (val[1] < r2) {
                t2 += r2 - val[1];
              }
              filter = $x.noUiSlider($.extend({
                start: [t1, t2],
                range: {min: t1, max: t2},
                connect: true
              }, opts), true);
              val = filter.val();
            }
            r1  = t1; r2 = t2;
          })();
          var updateSliderText = function(v1, v2) {
            $span1.text(formatDate(v1, false)); $span2.text(formatDate(v2, false));
          };
          updateSliderText(r1, r2);
          var updateSlider = function(e) {
            var val = filter.val();
            // turn off filter if in full range
            $td.data('filter', val[0] > slider_min() || val[1] < slider_max());
            var v1 = formatDate(val[0]), v2 = formatDate(val[1]), ival;
            if ($td.data('filter')) {
              ival = v1 + ' ... ' + v2;
              $input.attr('title', ival).val(ival).trigger('input');
            } else {
              $input.attr('title', '').val('');
            }
            updateSliderText(val[0], val[1]);
            if (e.type === 'slide') return;  // no searching when sliding only
            if (server) {
              table.column(i).search($td.data('filter') ? ival : '').draw();
              return;
            }
            table.draw();
          };
          filter.on({
            set: updateSlider,
            slide: updateSlider
          });
        }

        // server-side processing will be handled by R (or whatever server
        // language you use); the following code is only needed for client-side
        // processing
        if (server) {
          // if a search string has been pre-set, search now
          if (searchCol) searchColumn(i, searchCol).draw();
          return;
        }

        var customFilter = function(settings, data, dataIndex) {
          // there is no way to attach a search function to a specific table,
          // and we need to make sure a global search function is not applied to
          // all tables (i.e. a range filter in a previous table should not be
          // applied to the current table); we use the settings object to
          // determine if we want to perform searching on the current table,
          // since settings.sTableId will be different to different tables
          if (table.settings()[0] !== settings) return true;
          // no filter on this column or no need to filter this column
          if (typeof filter === 'undefined' || !$td.data('filter')) return true;

          var r = filter.val(), v, r0, r1;
          var i_data = function(i) {
            if (!colReorderEnabled()) return i;
            var order = table.colReorder.order(), k;
            for (k = 0; k < order.length; ++k) if (order[k] === i) return k;
            return i; // in theory it will never be here...
          }
          v = data[i_data(i)];
          if (type === 'number' || type === 'integer') {
            v = parseFloat(v);
            // how to handle NaN? currently exclude these rows
            if (isNaN(v)) return(false);
            r0 = parseFloat(scaleBack(r[0], scale))
            r1 = parseFloat(scaleBack(r[1], scale));
            if (v >= r0 && v <= r1) return true;
          } else if (type === 'date' || type === 'time') {
            v = new Date(v);
            r0 = new Date(r[0] / scale); r1 = new Date(r[1] / scale);
            if (v >= r0 && v <= r1) return true;
          } else if (type === 'factor') {
            if (r.length === 0 || inArray(v, r)) return true;
          } else if (type === 'logical') {
            if (r.length === 0) return true;
            if (inArray(v === '' ? 'na' : v, r)) return true;
          }
          return false;
        };

        $.fn.dataTable.ext.search.push(customFilter);

        // search for the preset search strings if it is non-empty
        if (searchCol) {
          if (inArray(type, ['factor', 'logical'])) {
            filter[0].selectize.setValue(JSON.parse(searchCol));
          } else if (type === 'character') {
            $input.trigger('input');
          } else if (inArray(type, ['number', 'integer', 'date', 'time'])) {
            $input.trigger('change');
          }
        }

      });

    }

    // highlight search keywords
    var highlight = function() {
      var body = $(table.table().body());
      // removing the old highlighting first
      body.unhighlight();

      // don't highlight the "not found" row, so we get the rows using the api
      if (table.rows({ filter: 'applied' }).data().length === 0) return;
      // highlight global search keywords
      body.highlight($.trim(table.search()).split(/\s+/));
      // then highlight keywords from individual column filters
      if (filterRow) filterRow.each(function(i, td) {
        var $td = $(td), type = $td.data('type');
        if (type !== 'character') return;
        var $input = $td.children('div').first().children('input');
        var column = table.column(i).nodes().to$(),
            val = $.trim($input.val());
        if (type !== 'character' || val === '') return;
        column.highlight(val.split(/\s+/));
      });
    };

    if (options.searchHighlight) {
      table
      .on('draw.dt.dth column-visibility.dt.dth column-reorder.dt.dth', highlight)
      .on('destroy', function() {
        // remove event handler
        table.off('draw.dt.dth column-visibility.dt.dth column-reorder.dt.dth');
      });

      // Set the option for escaping regex characters in our search string.  This will be used
      // for all future matching.
      jQuery.fn.highlight.options.escapeRegex = (!options.search || !options.search.regex);

      // initial highlight for state saved conditions and initial states
      highlight();
    }

    // run the callback function on the table instance
    if (typeof data.callback === 'function') data.callback(table);

    // double click to edit the cell, row, column, or all cells
    if (data.editable) table.on('dblclick.dt', 'tbody td', function(e) {
      // only bring up the editor when the cell itself is dbclicked, and ignore
      // other dbclick events bubbled up (e.g. from the <input>)
      if (e.target !== this) return;
      var target = [], immediate = false;
      switch (data.editable.target) {
        case 'cell':
          target = [this];
          immediate = true;  // edit will take effect immediately
          break;
        case 'row':
          target = table.cells(table.cell(this).index().row, '*').nodes();
          break;
        case 'column':
          target = table.cells('*', table.cell(this).index().column).nodes();
          break;
        case 'all':
          target = table.cells().nodes();
          break;
        default:
          throw 'The editable parameter must be "cell", "row", "column", or "all"';
      }
      var disableCols = data.editable.disable ? data.editable.disable.columns : null;
      var numericCols = data.editable.numeric;
      var areaCols = data.editable.area;
      var dateCols = data.editable.date;
      for (var i = 0; i < target.length; i++) {
        (function(cell, current) {
          var $cell = $(cell), html = $cell.html();
          var _cell = table.cell(cell), value = _cell.data(), index = _cell.index().column;
          var $input;
          if (inArray(index, numericCols)) {
            $input = $('<input type="number">');
          } else if (inArray(index, areaCols)) {
            $input = $('<textarea></textarea>');
          } else if (inArray(index, dateCols)) {
            $input = $('<input type="date">');
          } else {
            $input = $('<input type="text">');
          }
          if (!immediate) {
            $cell.data('input', $input).data('html', html);
            $input.attr('title', 'Hit Ctrl+Enter to finish editing, or Esc to cancel');
          }
          $input.val(value);
          if (inArray(index, disableCols)) {
            $input.attr('readonly', '').css('filter', 'invert(25%)');
          }
          $cell.empty().append($input);
          if (cell === current) $input.focus();
          $input.css('width', '100%');

          if (immediate) $input.on('blur', function(e) {
            var valueNew = $input.val();
            if (valueNew !== value) {
              _cell.data(valueNew);
              if (HTMLWidgets.shinyMode) {
                changeInput('cell_edit', [cellInfo(cell)], 'DT.cellInfo', null, {priority: 'event'});
              }
              // for server-side processing, users have to call replaceData() to update the table
              if (!server) table.draw(false);
            } else {
              $cell.html(html);
            }
          }).on('keyup', function(e) {
            // hit Escape to cancel editing
            if (e.keyCode === 27) $input.trigger('blur');
          });

          // bulk edit (row, column, or all)
          if (!immediate) $input.on('keyup', function(e) {
            var removeInput = function($cell, restore) {
              $cell.data('input').remove();
              if (restore) $cell.html($cell.data('html'));
            }
            if (e.keyCode === 27) {
              for (var i = 0; i < target.length; i++) {
                removeInput($(target[i]), true);
              }
            } else if (e.keyCode === 13 && e.ctrlKey) {
              // Ctrl + Enter
              var cell, $cell, _cell, cellData = [];
              for (var i = 0; i < target.length; i++) {
                cell = target[i]; $cell = $(cell); _cell = table.cell(cell);
                _cell.data($cell.data('input').val());
                HTMLWidgets.shinyMode && cellData.push(cellInfo(cell));
                removeInput($cell, false);
              }
              if (HTMLWidgets.shinyMode) {
                changeInput('cell_edit', cellData, 'DT.cellInfo', null, {priority: "event"});
              }
              if (!server) table.draw(false);
            }
          });
        })(target[i], this);
      }
    });

    // interaction with shiny
    if (!HTMLWidgets.shinyMode && !crosstalkOptions.group) return;

    var methods = {};
    var shinyData = {};

    methods.updateCaption = function(caption) {
      if (!caption) return;
      $table.children('caption').replaceWith(caption);
    }

    // register clear functions to remove input values when the table is removed
    instance.clearInputs = {};

    var changeInput = function(id, value, type, noCrosstalk, opts) {
      var event = id;
      id = el.id + '_' + id;
      if (type) id = id + ':' + type;
      // do not update if the new value is the same as old value
      if (event !== 'cell_edit' && !/_clicked$/.test(event) && shinyData.hasOwnProperty(id) && shinyData[id] === JSON.stringify(value))
        return;
      shinyData[id] = JSON.stringify(value);
      if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
        Shiny.setInputValue(id, value, opts);
        if (!instance.clearInputs[id]) instance.clearInputs[id] = function() {
          Shiny.setInputValue(id, null);
        }
      }

      // HACK
      if (event === "rows_selected" && !noCrosstalk) {
        if (crosstalkOptions.group) {
          var keys = crosstalkOptions.key;
          var selectedKeys = null;
          if (value) {
            selectedKeys = [];
            for (var i = 0; i < value.length; i++) {
              // The value array's contents use 1-based row numbers, so we must
              // convert to 0-based before indexing into the keys array.
              selectedKeys.push(keys[value[i] - 1]);
            }
          }
          instance.ctselectHandle.set(selectedKeys);
        }
      }
    };

    var addOne = function(x) {
      return x.map(function(i) { return 1 + i; });
    };

    var unique = function(x) {
      var ux = [];
      $.each(x, function(i, el){
        if ($.inArray(el, ux) === -1) ux.push(el);
      });
      return ux;
    }

    // change the row index of a cell
    var tweakCellIndex = function(cell) {
      var info = cell.index();
      // some cell may not be valid. e.g, #759
      // when using the RowGroup extension, datatables will
      // generate the row label and the cells are not part of
      // the data thus contain no row/col info
      if (info === undefined)
        return {row: null, col: null};
      if (server) {
        info.row = DT_rows_current[info.row];
      } else {
        info.row += 1;
      }
      return {row: info.row, col: info.column};
    }

    var cleanSelectedValues = function() {
      changeInput('rows_selected', []);
      changeInput('columns_selected', []);
      changeInput('cells_selected', transposeArray2D([]), 'shiny.matrix');
    }
    // #828 we should clean the selection on the server-side when the table reloads
    cleanSelectedValues();

    // a flag to indicates if select extension is initialized or not
    var flagSelectExt = table.settings()[0]._select !== undefined;
    // the Select extension should only be used in the client mode and
    // when the selection.mode is set to none
    if (data.selection.mode === 'none' && !server && flagSelectExt) {
      var updateRowsSelected = function() {
        var rows = table.rows({selected: true});
        var selected = [];
        $.each(rows.indexes().toArray(), function(i, v) {
          selected.push(v + 1);
        });
        changeInput('rows_selected', selected);
      }
      var updateColsSelected = function() {
        var columns = table.columns({selected: true});
        changeInput('columns_selected', columns.indexes().toArray());
      }
      var updateCellsSelected = function() {
        var cells = table.cells({selected: true});
        var selected = [];
        cells.every(function() {
          var row = this.index().row;
          var col = this.index().column;
          selected = selected.concat([[row + 1, col]]);
        });
        changeInput('cells_selected', transposeArray2D(selected), 'shiny.matrix');
      }
      table.on('select deselect', function(e, dt, type, indexes) {
        updateRowsSelected();
        updateColsSelected();
        updateCellsSelected();
      })
    }

    var selMode = data.selection.mode, selTarget = data.selection.target;
    var selDisable = data.selection.selectable === false;
    if (inArray(selMode, ['single', 'multiple'])) {
      var selClass = inArray(data.style, ['bootstrap', 'bootstrap4']) ? 'active' : 'selected';
      // selected1: row indices; selected2: column indices
      var initSel = function(x) {
        if (x === null || typeof x === 'boolean' || selTarget === 'cell') {
          return {rows: [], cols: []};
        } else if (selTarget === 'row') {
          return {rows: $.makeArray(x), cols: []};
        } else if (selTarget === 'column') {
          return {rows: [], cols: $.makeArray(x)};
        } else if (selTarget === 'row+column') {
          return {rows: $.makeArray(x.rows), cols: $.makeArray(x.cols)};
        }
      }
      var selected = data.selection.selected;
      var selected1 = initSel(selected).rows, selected2 = initSel(selected).cols;
      // selectable should contain either all positive or all non-positive values, not both
      // positive values indicate "selectable" while non-positive values means "nonselectable"
      // the assertion is performed on R side. (only column indicides could be zero which indicates
      // the row name)
      var selectable = data.selection.selectable;
      var selectable1 = initSel(selectable).rows, selectable2 = initSel(selectable).cols;

      // After users reorder the rows or filter the table, we cannot use the table index
      // directly. Instead, we need this function to find out the rows between the two clicks.
      // If user filter the table again between the start click and the end click, the behavior
      // would be undefined, but it should not be a problem.
      var shiftSelRowsIndex = function(start, end) {
        var indexes = server ? DT_rows_all : table.rows({ search: 'applied' }).indexes().toArray();
        start = indexes.indexOf(start); end = indexes.indexOf(end);
        // if start is larger than end, we need to swap
        if (start > end) {
          var tmp = end; end = start; start = tmp;
        }
        return indexes.slice(start, end + 1);
      }

      var serverRowIndex = function(clientRowIndex) {
        return server ? DT_rows_current[clientRowIndex] : clientRowIndex + 1;
      }

      // row, column, or cell selection
      var lastClickedRow;
      if (inArray(selTarget, ['row', 'row+column'])) {
        // Get the current selected rows. It will also
        // update the selected1's value based on the current row selection state
        // Note we can't put this function inside selectRows() directly,
        // the reason is method.selectRows() will override selected1's value but this
        // function will add rows to selected1 (keep the existing selection), which is
        // inconsistent with column and cell selection.
        var selectedRows = function() {
          var rows = table.rows('.' + selClass);
          var idx = rows.indexes().toArray();
          if (!server) {
            selected1 = addOne(idx);
            return selected1;
          }
          idx = idx.map(function(i) {
            return DT_rows_current[i];
          });
          selected1 = selMode === 'multiple' ? unique(selected1.concat(idx)) : idx;
          return selected1;
        }
        // Change selected1's value based on selectable1, then refresh the row state
        var onlyKeepSelectableRows = function() {
          if (selDisable) { // users can't select; useful when only want backend select
            selected1 = [];
            return;
          }
          if (selectable1.length === 0) return;
          var nonselectable = selectable1[0] <= 0;
          if (nonselectable) {
            // should make selectable1 positive
            selected1 = $(selected1).not(selectable1.map(function(i) { return -i; })).get();
          } else {
            selected1 = $(selected1).filter(selectable1).get();
          }
        }
        // Change selected1's value based on selectable1, then
        // refresh the row selection state according to values in selected1
        var selectRows = function(ignoreSelectable) {
          if (!ignoreSelectable) onlyKeepSelectableRows();
          table.$('tr.' + selClass).removeClass(selClass);
          if (selected1.length === 0) return;
          if (server) {
            table.rows({page: 'current'}).every(function() {
              if (inArray(DT_rows_current[this.index()], selected1)) {
                $(this.node()).addClass(selClass);
              }
            });
          } else {
            var selected0 = selected1.map(function(i) { return i - 1; });
            $(table.rows(selected0).nodes()).addClass(selClass);
          }
        }
        table.on('mousedown.dt', 'tbody tr', function(e) {
          var $this = $(this), thisRow = table.row(this);
          if (selMode === 'multiple') {
            if (e.shiftKey && lastClickedRow !== undefined) {
              // select or de-select depends on the last clicked row's status
              var flagSel = !$this.hasClass(selClass);
              var crtClickedRow = serverRowIndex(thisRow.index());
              if (server) {
                var rowsIndex = shiftSelRowsIndex(lastClickedRow, crtClickedRow);
                // update current page's selClass
                rowsIndex.map(function(i) {
                  var rowIndex = DT_rows_current.indexOf(i);
                  if (rowIndex >= 0) {
                    var row = table.row(rowIndex).nodes().to$();
                    var flagRowSel = !row.hasClass(selClass);
                    if (flagSel === flagRowSel) row.toggleClass(selClass);
                  }
                });
                // update selected1
                if (flagSel) {
                  selected1 = unique(selected1.concat(rowsIndex));
                } else {
                  selected1 = selected1.filter(function(index) {
                    return !inArray(index, rowsIndex);
                  });
                }
              } else {
                // js starts from 0
                shiftSelRowsIndex(lastClickedRow - 1, crtClickedRow - 1).map(function(value) {
                  var row = table.row(value).nodes().to$();
                  var flagRowSel = !row.hasClass(selClass);
                  if (flagSel === flagRowSel) row.toggleClass(selClass);
                });
              }
              e.preventDefault();
            } else {
              $this.toggleClass(selClass);
            }
          } else {
            if ($this.hasClass(selClass)) {
              $this.removeClass(selClass);
            } else {
              table.$('tr.' + selClass).removeClass(selClass);
              $this.addClass(selClass);
            }
          }
          if (server && !$this.hasClass(selClass)) {
            var id = DT_rows_current[thisRow.index()];
            // remove id from selected1 since its class .selected has been removed
            if (inArray(id, selected1)) selected1.splice($.inArray(id, selected1), 1);
          }
          selectedRows(); // update selected1's value based on selClass
          selectRows(false); // only keep the selectable rows
          changeInput('rows_selected', selected1);
          changeInput('row_last_clicked', serverRowIndex(thisRow.index()), null, null, {priority: 'event'});
          lastClickedRow = serverRowIndex(thisRow.index());
        });
        selectRows(false);  // in case users have specified pre-selected rows
        // restore selected rows after the table is redrawn (e.g. sort/search/page);
        // client-side tables will preserve the selections automatically; for
        // server-side tables, we have to *real* row indices are in `selected1`
        changeInput('rows_selected', selected1);
        if (server) table.on('draw.dt', function(e) { selectRows(false); });
        methods.selectRows = function(selected, ignoreSelectable) {
          selected1 = $.makeArray(selected);
          selectRows(ignoreSelectable);
          changeInput('rows_selected', selected1);
        }
      }

      if (inArray(selTarget, ['column', 'row+column'])) {
        if (selTarget === 'row+column') {
          $(table.columns().footer()).css('cursor', 'pointer');
        }
        // update selected2's value based on selectable2
        var onlyKeepSelectableCols = function() {
          if (selDisable) { // users can't select; useful when only want backend select
            selected2 = [];
            return;
          }
          if (selectable2.length === 0) return;
          var nonselectable = selectable2[0] <= 0;
          if (nonselectable) {
            // need to make selectable2 positive
            selected2 = $(selected2).not(selectable2.map(function(i) { return -i; })).get();
          } else {
            selected2 = $(selected2).filter(selectable2).get();
          }
        }
        // update selected2 and then
        // refresh the col selection state according to values in selected2
        var selectCols = function(ignoreSelectable) {
          if (!ignoreSelectable) onlyKeepSelectableCols();
          // if selected2 is not a valide index (e.g., larger than the column number)
          // table.columns(selected2) will fail and result in a blank table
          // this is different from the table.rows(), where the out-of-range indexes
          // doesn't affect at all
          selected2 = $(selected2).filter(table.columns().indexes()).get();
          table.columns().nodes().flatten().to$().removeClass(selClass);
          if (selected2.length > 0)
            table.columns(selected2).nodes().flatten().to$().addClass(selClass);
        }
        var callback = function() {
          var colIdx = selTarget === 'column' ? table.cell(this).index().column :
              $.inArray(this, table.columns().footer()),
              thisCol = $(table.column(colIdx).nodes());
          if (colIdx === -1) return;
          if (thisCol.hasClass(selClass)) {
            thisCol.removeClass(selClass);
            selected2.splice($.inArray(colIdx, selected2), 1);
          } else {
            if (selMode === 'single') $(table.cells().nodes()).removeClass(selClass);
            thisCol.addClass(selClass);
            selected2 = selMode === 'single' ? [colIdx] : unique(selected2.concat([colIdx]));
          }
          selectCols(false); // update selected2 based on selectable
          changeInput('columns_selected', selected2);
        }
        if (selTarget === 'column') {
          $(table.table().body()).on('click.dt', 'td', callback);
        } else {
          $(table.table().footer()).on('click.dt', 'tr th', callback);
        }
        selectCols(false);  // in case users have specified pre-selected columns
        changeInput('columns_selected', selected2);
        if (server) table.on('draw.dt', function(e) { selectCols(false); });
        methods.selectColumns = function(selected, ignoreSelectable) {
          selected2 = $.makeArray(selected);
          selectCols(ignoreSelectable);
          changeInput('columns_selected', selected2);
        }
      }

      if (selTarget === 'cell') {
        var selected3 = [], selectable3 = [];
        if (selected !== null) selected3 = selected;
        if (selectable !== null && typeof selectable !== 'boolean') selectable3 = selectable;
        var findIndex = function(ij, sel) {
          for (var i = 0; i < sel.length; i++) {
            if (ij[0] === sel[i][0] && ij[1] === sel[i][1]) return i;
          }
          return -1;
        }
         // Change selected3's value based on selectable3, then refresh the cell state
        var onlyKeepSelectableCells = function() {
          if (selDisable) { // users can't select; useful when only want backend select
            selected3 = [];
            return;
          }
          if (selectable3.length === 0) return;
          var nonselectable = selectable3[0][0] <= 0;
          var out = [];
          if (nonselectable) {
            selected3.map(function(ij) {
              // should make selectable3 positive
              if (findIndex([-ij[0], -ij[1]], selectable3) === -1) { out.push(ij); }
            });
          } else {
            selected3.map(function(ij) {
              if (findIndex(ij, selectable3) > -1) { out.push(ij); }
            });
          }
          selected3 = out;
        }
        // Change selected3's value based on selectable3, then
        // refresh the cell selection state according to values in selected3
        var selectCells = function(ignoreSelectable) {
          if (!ignoreSelectable) onlyKeepSelectableCells();
          table.$('td.' + selClass).removeClass(selClass);
          if (selected3.length === 0) return;
          if (server) {
            table.cells({page: 'current'}).every(function() {
              var info = tweakCellIndex(this);
              if (findIndex([info.row, info.col], selected3) > -1)
                $(this.node()).addClass(selClass);
            });
          } else {
            selected3.map(function(ij) {
              $(table.cell(ij[0] - 1, ij[1]).node()).addClass(selClass);
            });
          }
        };
        table.on('click.dt', 'tbody td', function() {
          var $this = $(this), info = tweakCellIndex(table.cell(this));
          if ($this.hasClass(selClass)) {
            $this.removeClass(selClass);
            selected3.splice(findIndex([info.row, info.col], selected3), 1);
          } else {
            if (selMode === 'single') $(table.cells().nodes()).removeClass(selClass);
            $this.addClass(selClass);
            selected3 = selMode === 'single' ? [[info.row, info.col]] :
              unique(selected3.concat([[info.row, info.col]]));
          }
          selectCells(false); // must call this to update selected3 based on selectable3
          changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
        });
        selectCells(false);  // in case users have specified pre-selected columns
        changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');

        if (server) table.on('draw.dt', function(e) { selectCells(false); });
        methods.selectCells = function(selected, ignoreSelectable) {
          selected3 = selected ? selected : [];
          selectCells(ignoreSelectable);
          changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
        }
      }
    }

    // expose some table info to Shiny
    var updateTableInfo = function(e, settings) {
      // TODO: is anyone interested in the page info?
      // changeInput('page_info', table.page.info());
      var updateRowInfo = function(id, modifier) {
        var idx;
        if (server) {
          idx = modifier.page === 'current' ? DT_rows_current : DT_rows_all;
        } else {
          var rows = table.rows($.extend({
            search: 'applied',
            page: 'all'
          }, modifier));
          idx = addOne(rows.indexes().toArray());
        }
        changeInput('rows' + '_' + id, idx);
      };
      updateRowInfo('current', {page: 'current'});
      updateRowInfo('all', {});
    }
    table.on('draw.dt', updateTableInfo);
    updateTableInfo();

    // state info
    table.on('draw.dt column-visibility.dt', function() {
      changeInput('state', table.state());
    });
    changeInput('state', table.state());

    // search info
    var updateSearchInfo = function() {
      changeInput('search', table.search());
      if (filterRow) changeInput('search_columns', filterRow.toArray().map(function(td) {
        return $(td).find('input').first().val();
      }));
    }
    table.on('draw.dt', updateSearchInfo);
    updateSearchInfo();

    var cellInfo = function(thiz) {
      var info = tweakCellIndex(table.cell(thiz));
      info.value = table.cell(thiz).data();
      return info;
    }
    // the current cell clicked on
    table.on('click.dt', 'tbody td', function() {
      changeInput('cell_clicked', cellInfo(this), null, null, {priority: 'event'});
    })
    changeInput('cell_clicked', {});

    // do not trigger table selection when clicking on links unless they have classes
    table.on('click.dt', 'tbody td a', function(e) {
      if (this.className === '') e.stopPropagation();
    });

    methods.addRow = function(data, rowname, resetPaging) {
      var n = table.columns().indexes().length, d = n - data.length;
      if (d === 1) {
        data = rowname.concat(data)
      } else if (d !== 0) {
        console.log(data);
        console.log(table.columns().indexes());
        throw 'New data must be of the same length as current data (' + n + ')';
      };
      table.row.add(data).draw(resetPaging);
    }

    methods.updateSearch = function(keywords) {
      if (keywords.global !== null)
        $(table.table().container()).find('input[type=search]').first()
             .val(keywords.global).trigger('input');
      var columns = keywords.columns;
      if (!filterRow || columns === null) return;
      filterRow.toArray().map(function(td, i) {
        var v = typeof columns === 'string' ? columns : columns[i];
        if (typeof v === 'undefined') {
          console.log('The search keyword for column ' + i + ' is undefined')
          return;
        }
        $(td).find('input').first().val(v);
        searchColumn(i, v);
      });
      table.draw();
    }

    methods.hideCols = function(hide, reset) {
      if (reset) table.columns().visible(true, false);
      table.columns(hide).visible(false);
    }

    methods.showCols = function(show, reset) {
      if (reset) table.columns().visible(false, false);
      table.columns(show).visible(true);
    }

    methods.colReorder = function(order, origOrder) {
      table.colReorder.order(order, origOrder);
    }

    methods.selectPage = function(page) {
      if (table.page.info().pages < page || page < 1) {
        throw 'Selected page is out of range';
      };
      table.page(page - 1).draw(false);
    }

    methods.reloadData = function(resetPaging, clearSelection) {
      // empty selections first if necessary
      if (methods.selectRows && inArray('row', clearSelection)) methods.selectRows([]);
      if (methods.selectColumns && inArray('column', clearSelection)) methods.selectColumns([]);
      if (methods.selectCells && inArray('cell', clearSelection)) methods.selectCells([]);
      table.ajax.reload(null, resetPaging);
    }

    // update table filters (set new limits of sliders)
    methods.updateFilters = function(newProps) {
      // loop through each filter in the filter row
      filterRow.each(function(i, td) {
        var k = i;
        if (filterRow.length > newProps.length) {
          if (i === 0) return;  // first column is row names
          k = i - 1;
        }
        // Update the filters to reflect the updated data.
        // Allow "falsy" (e.g. NULL) to signify a no-op.
        if (newProps[k]) {
          setFilterProps(td, newProps[k]);
        }
      });
    };

    table.shinyMethods = methods;
  },
  resize: function(el, width, height, instance) {
    if (instance.data) this.renderValue(el, instance.data, instance);

    // dynamically adjust height if fillContainer = TRUE
    if (instance.fillContainer)
      this.fillAvailableHeight(el, height);

    this.adjustWidth(el);
  },

  // dynamically set the scroll body to fill available height
  // (used with fillContainer = TRUE)
  fillAvailableHeight: function(el, availableHeight) {

    // see how much of the table is occupied by header/footer elements
    // and use that to compute a target scroll body height
    var dtWrapper = $(el).find('div.dataTables_wrapper');
    var dtScrollBody = $(el).find($('div.dataTables_scrollBody'));
    var framingHeight = dtWrapper.innerHeight() - dtScrollBody.innerHeight();
    var scrollBodyHeight = availableHeight - framingHeight;

    // we need to set `max-height` to none as datatables library now sets this
    // to a fixed height, disabling the ability to resize to fill the window,
    // as it will be set to a fixed 100px under such circumstances, e.g., RStudio IDE,
    // or FlexDashboard
    // see https://github.com/rstudio/DT/issues/951#issuecomment-1026464509
    dtScrollBody.css('max-height', 'none');
    // set the height
    dtScrollBody.height(scrollBodyHeight + 'px');
  },

  // adjust the width of columns; remove the hard-coded widths on table and the
  // scroll header when scrollX/Y are enabled
  adjustWidth: function(el) {
    var $el = $(el), table = $el.data('datatable');
    if (table) table.columns.adjust();
    $el.find('.dataTables_scrollHeadInner').css('width', '')
        .children('table').css('margin-left', '');
  }
});

  if (!HTMLWidgets.shinyMode) return;

  Shiny.addCustomMessageHandler('datatable-calls', function(data) {
    var id = data.id;
    var el = document.getElementById(id);
    var table = el ? $(el).data('datatable') : null;
    if (!table) {
      console.log("Couldn't find table with id " + id);
      return;
    }

    var methods = table.shinyMethods, call = data.call;
    if (methods[call.method]) {
      methods[call.method].apply(table, call.args);
    } else {
      console.log("Unknown method " + call.method);
    }
  });

})();
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<h1 class="title toc-ignore">Replication code for “Financial Crises and
the Selection and Survival of Women Finance Ministers” American
Political Science Review</h1>
<h4 class="author">Brenna Armstrong, Tiffany D. Barnes, Daina Chiba, and
Diana Z. O’Brien</h4>
<h4 class="date">2023/06/22</h4>

</div>


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<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>To knit this RMarkdown file, please place the replication data files
(<code>ABCO_data1.csv</code> and <code>ABCO_data2.csv</code>) in the
same folder as this file. You would need to have the <code>pacman</code>
package installed before running the code. All the other necessary R
packages will be installed automatically if not installed yet.</p>
<p>The file has four main sections:<br />
- <strong>1. Descriptive Analysis</strong> provides descriptions of the
data such as the choropleth map in the main manuscript (Figure 1) and
various statistics mentioned in the text.<br />
- <strong>2. Women’s Initial Access</strong> replicates our analyses of
women’s entrance into finance ministries that are presented in the main
text or in the online appendix.<br />
- <strong>3. Finance Minister Tenure</strong> replicates our analyses of
risk of a finance minister leaving office that are presented in the main
text or in the online appendix.<br />
- <strong>4. Minister Background</strong> replicates our analyses of
ministers’ education and professional background that are discussed in
the main text and in the online appendix.</p>
<p>The results in the paper were generated using R ver 4.3.0. The
versions of the R packages used in the analyses are provided in the
<strong>Session Information</strong> section at the bottom of the
RMarkdown output (<code>ABCO_Replication.html</code>).</p>
<div id="preparation" class="section level2">
<h2>Preparation</h2>
<p>Load relevant packages and define some colors.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="do">## Unload all loaded packages if any</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_unload</span>(pacman<span class="sc">::</span><span class="fu">p_loaded</span>(), <span class="at">character.only =</span> <span class="cn">TRUE</span>)</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="do">## Load necessary packages</span></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(tidyverse, tidylog, </span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>               DT, maps, countrycode, pltesim, </span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>               stargazer, multiwayvcov, lmtest, survival, </span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a>               knitr, kableExtra, rcartocolor, ggdist, gghalves, patchwork)</span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a></span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a><span class="do">## custom colors</span></span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>my_pal <span class="ot">&lt;-</span> <span class="fu">carto_pal</span>(<span class="at">n =</span> <span class="dv">8</span>, <span class="at">name =</span> <span class="st">&quot;Bold&quot;</span>)[<span class="fu">c</span>(<span class="dv">7</span>, <span class="dv">3</span>, <span class="dv">2</span>, <span class="dv">1</span>)]</span></code></pre></div>
<p>Load the data.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="do">## Data for main analyses</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;ABCO_data1.csv&quot;</span>)</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="do">## Data for background information analyses</span></span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>df_appC <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;ABCO_data2.csv&quot;</span>)</span></code></pre></div>
<p>Define helper functions</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="do">## Functions to calculate clustered standard errors from the fitted object</span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="do">### For hazard rate</span></span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a>get_clse_hr <span class="ot">&lt;-</span> <span class="cf">function</span>(fit){</span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a>  <span class="fu">sqrt</span>(<span class="fu">diag</span>(fit <span class="sc">$</span> var))</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a>  }</span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a></span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a><span class="do">### For hazard ratio (exponentiated coefficients)</span></span>
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a>get_clse_or <span class="ot">&lt;-</span> <span class="cf">function</span>(fit){</span>
<span id="cb3-9"><a href="#cb3-9" tabindex="-1"></a>  or <span class="ot">&lt;-</span> <span class="fu">exp</span>(<span class="fu">coef</span>(fit))</span>
<span id="cb3-10"><a href="#cb3-10" tabindex="-1"></a>  var.diag <span class="ot">&lt;-</span> <span class="fu">diag</span>(fit <span class="sc">$</span> var)</span>
<span id="cb3-11"><a href="#cb3-11" tabindex="-1"></a>  <span class="fu">return</span>(<span class="fu">sqrt</span>(or<span class="sc">^</span><span class="dv">2</span> <span class="sc">*</span> var.diag))</span>
<span id="cb3-12"><a href="#cb3-12" tabindex="-1"></a>}</span>
<span id="cb3-13"><a href="#cb3-13" tabindex="-1"></a></span>
<span id="cb3-14"><a href="#cb3-14" tabindex="-1"></a><span class="do">## Functions to extract relevant results</span></span>
<span id="cb3-15"><a href="#cb3-15" tabindex="-1"></a>extract_hr <span class="ot">&lt;-</span> <span class="cf">function</span>(fit){</span>
<span id="cb3-16"><a href="#cb3-16" tabindex="-1"></a>  df <span class="ot">&lt;-</span> <span class="fu">summary</span>(fit) <span class="sc">$</span> conf.int</span>
<span id="cb3-17"><a href="#cb3-17" tabindex="-1"></a>  pe <span class="ot">&lt;-</span> df[<span class="fu">rownames</span>(df) <span class="sc">==</span> <span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;exp(coef)&quot;</span>]</span>
<span id="cb3-18"><a href="#cb3-18" tabindex="-1"></a>  low <span class="ot">&lt;-</span> df[<span class="fu">rownames</span>(df) <span class="sc">==</span> <span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;lower .95&quot;</span>]</span>
<span id="cb3-19"><a href="#cb3-19" tabindex="-1"></a>  up <span class="ot">&lt;-</span> df[<span class="fu">rownames</span>(df) <span class="sc">==</span> <span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;upper .95&quot;</span>]</span>
<span id="cb3-20"><a href="#cb3-20" tabindex="-1"></a>  <span class="fu">data.frame</span>(<span class="at">pe =</span> pe, <span class="at">low =</span> low, <span class="at">up =</span> up)</span>
<span id="cb3-21"><a href="#cb3-21" tabindex="-1"></a>}</span>
<span id="cb3-22"><a href="#cb3-22" tabindex="-1"></a></span>
<span id="cb3-23"><a href="#cb3-23" tabindex="-1"></a><span class="do">## Function to add AIC to the model object (so that stargazer recognizes them)</span></span>
<span id="cb3-24"><a href="#cb3-24" tabindex="-1"></a>add_AIC <span class="ot">&lt;-</span> <span class="cf">function</span>(fit){</span>
<span id="cb3-25"><a href="#cb3-25" tabindex="-1"></a>  fit <span class="sc">$</span> AIC <span class="ot">&lt;-</span> <span class="fu">AIC</span>(fit)</span>
<span id="cb3-26"><a href="#cb3-26" tabindex="-1"></a>  <span class="fu">return</span>(fit)</span>
<span id="cb3-27"><a href="#cb3-27" tabindex="-1"></a>}</span>
<span id="cb3-28"><a href="#cb3-28" tabindex="-1"></a></span>
<span id="cb3-29"><a href="#cb3-29" tabindex="-1"></a></span>
<span id="cb3-30"><a href="#cb3-30" tabindex="-1"></a><span class="do">## Functions to test the PH assumption </span></span>
<span id="cb3-31"><a href="#cb3-31" tabindex="-1"></a></span>
<span id="cb3-32"><a href="#cb3-32" tabindex="-1"></a><span class="do">## The following functions are taken from Park and Hendry&#39;s (2015) replication</span></span>
<span id="cb3-33"><a href="#cb3-33" tabindex="-1"></a><span class="do">## package (park_hendry_ajps03.r). We modified them so that we focus on </span></span>
<span id="cb3-34"><a href="#cb3-34" tabindex="-1"></a><span class="do">## KM transformation (default) and rank transformation. </span></span>
<span id="cb3-35"><a href="#cb3-35" tabindex="-1"></a></span>
<span id="cb3-36"><a href="#cb3-36" tabindex="-1"></a>zphTable <span class="ot">&lt;-</span> <span class="cf">function</span>(cox.fit, digits) {</span>
<span id="cb3-37"><a href="#cb3-37" tabindex="-1"></a>  <span class="co"># Performs cox.zph function from the survival package using the four</span></span>
<span id="cb3-38"><a href="#cb3-38" tabindex="-1"></a>  <span class="co">#   available time transformations and outputs one table with side-by-side</span></span>
<span id="cb3-39"><a href="#cb3-39" tabindex="-1"></a>  <span class="co">#   presentation of results</span></span>
<span id="cb3-40"><a href="#cb3-40" tabindex="-1"></a>  <span class="co"># Args:</span></span>
<span id="cb3-41"><a href="#cb3-41" tabindex="-1"></a>  <span class="co">#   cox.fit: fit of a Cox model using the coxph function</span></span>
<span id="cb3-42"><a href="#cb3-42" tabindex="-1"></a>  <span class="co">#   digits:  desired number of digits for presentation of results</span></span>
<span id="cb3-43"><a href="#cb3-43" tabindex="-1"></a>  tab <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">cbind</span>(<span class="fu">cox.zph</span>(cox.fit, <span class="at">transform =</span> <span class="st">&#39;km&#39;</span>)<span class="sc">$</span>table,</span>
<span id="cb3-44"><a href="#cb3-44" tabindex="-1"></a>                     <span class="fu">cox.zph</span>(cox.fit, <span class="at">transform =</span> <span class="st">&#39;rank&#39;</span>)<span class="sc">$</span>table), <span class="at">digits =</span> digits)</span>
<span id="cb3-45"><a href="#cb3-45" tabindex="-1"></a>  cols <span class="ot">&lt;-</span> <span class="fu">colnames</span>(<span class="fu">cox.zph</span>(cox.fit)<span class="sc">$</span>table)</span>
<span id="cb3-46"><a href="#cb3-46" tabindex="-1"></a>  <span class="fu">colnames</span>(tab) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">paste</span>(<span class="st">&#39;km.&#39;</span>, cols, <span class="at">sep =</span> <span class="st">&#39;&#39;</span>),</span>
<span id="cb3-47"><a href="#cb3-47" tabindex="-1"></a>                     <span class="fu">paste</span>(<span class="st">&#39;rank.&#39;</span>, cols, <span class="at">sep =</span> <span class="st">&#39;&#39;</span>))    </span>
<span id="cb3-48"><a href="#cb3-48" tabindex="-1"></a>  tab</span>
<span id="cb3-49"><a href="#cb3-49" tabindex="-1"></a>}</span>
<span id="cb3-50"><a href="#cb3-50" tabindex="-1"></a></span>
<span id="cb3-51"><a href="#cb3-51" tabindex="-1"></a>calcNPH <span class="ot">&lt;-</span> <span class="cf">function</span>(fit, <span class="at">tfunc =</span> <span class="st">&quot;id&quot;</span>, <span class="at">seed =</span> <span class="dv">12345</span>, <span class="at">nsims =</span> <span class="dv">1000</span>){</span>
<span id="cb3-52"><a href="#cb3-52" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;id&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) t}</span>
<span id="cb3-53"><a href="#cb3-53" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;log&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) <span class="fu">log</span>(t)}</span>
<span id="cb3-54"><a href="#cb3-54" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;sqrt&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) <span class="fu">sqrt</span>(t)}</span>
<span id="cb3-55"><a href="#cb3-55" tabindex="-1"></a>  <span class="fu">set.seed</span>(seed)</span>
<span id="cb3-56"><a href="#cb3-56" tabindex="-1"></a>  simb <span class="ot">&lt;-</span> MASS<span class="sc">::</span><span class="fu">mvrnorm</span>(<span class="at">n =</span> nsims, <span class="at">mu =</span> <span class="fu">coef</span>(fit), <span class="at">Sigma =</span> <span class="fu">vcov</span>(fit))</span>
<span id="cb3-57"><a href="#cb3-57" tabindex="-1"></a>  col_need <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">colnames</span>(simb) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;tt(crisis_mon)&quot;</span>))</span>
<span id="cb3-58"><a href="#cb3-58" tabindex="-1"></a>  simb <span class="ot">&lt;-</span> simb[, col_need]</span>
<span id="cb3-59"><a href="#cb3-59" tabindex="-1"></a>  time <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="at">from =</span> <span class="dv">1</span>, <span class="at">to =</span> <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">3</span>)</span>
<span id="cb3-60"><a href="#cb3-60" tabindex="-1"></a>  xmat <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, <span class="fu">ft</span>(time))</span>
<span id="cb3-61"><a href="#cb3-61" tabindex="-1"></a>  xb <span class="ot">&lt;-</span> <span class="fu">exp</span>(xmat <span class="sc">%*%</span> <span class="fu">t</span>(simb))</span>
<span id="cb3-62"><a href="#cb3-62" tabindex="-1"></a>  cut_points <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.025</span>, <span class="fl">0.05</span>, <span class="fl">0.5</span>, <span class="fl">0.95</span>, <span class="fl">0.975</span>)</span>
<span id="cb3-63"><a href="#cb3-63" tabindex="-1"></a>  est <span class="ot">&lt;-</span> <span class="fu">apply</span>(xb, <span class="dv">1</span>, quantile, cut_points) <span class="sc">|&gt;</span> <span class="fu">t</span>()</span>
<span id="cb3-64"><a href="#cb3-64" tabindex="-1"></a>  est <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">time =</span> time, est) <span class="sc">|&gt;</span> <span class="fu">as.data.frame</span>()</span>
<span id="cb3-65"><a href="#cb3-65" tabindex="-1"></a>  <span class="fu">colnames</span>(est) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;time&quot;</span>, <span class="st">&quot;low95&quot;</span>, <span class="st">&quot;low90&quot;</span>, <span class="st">&quot;median&quot;</span>, <span class="st">&quot;upp90&quot;</span>, <span class="st">&quot;upp95&quot;</span>)</span>
<span id="cb3-66"><a href="#cb3-66" tabindex="-1"></a>  <span class="fu">return</span>(est)</span>
<span id="cb3-67"><a href="#cb3-67" tabindex="-1"></a>}</span></code></pre></div>
</div>
</div>
<div id="descriptive-analysis" class="section level1">
<h1>1. Descriptive Analysis</h1>
<div id="choropleth-map-figure-1" class="section level2">
<h2>Choropleth Map (Figure 1)</h2>
<p>Load world map data.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>world <span class="ot">&lt;-</span> <span class="fu">map_data</span>(<span class="st">&quot;world&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a>    <span class="at">iso3c =</span> <span class="fu">countrycode</span>(</span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a>    <span class="at">sourcevar =</span> region, </span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>    <span class="at">origin =</span> <span class="st">&quot;country.name&quot;</span>, </span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a>    <span class="at">destination =</span> <span class="st">&quot;iso3c&quot;</span>)</span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a>    )</span></code></pre></div>
<pre><code>## Warning: There was 1 warning in `.fun()`.
## ℹ In argument: `iso3c = countrycode(sourcevar = region, origin =
##   &quot;country.name&quot;, destination = &quot;iso3c&quot;)`.
## Caused by warning:
## ! Some values were not matched unambiguously: Ascension Island, Azores, Barbuda, Bonaire, Canary Islands, Chagos Archipelago, Grenadines, Heard Island, Kosovo, Madeira Islands, Micronesia, Saba, Saint Martin, Siachen Glacier, Sint Eustatius, Virgin Islands</code></pre>
<pre><code>## mutate: new variable &#39;iso3c&#39; (character) with 234 unique values and &lt;1% NA</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="do">## Manually code ISO code for a few of them. </span></span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>world <span class="sc">$</span> iso3c[ world <span class="sc">$</span> region <span class="sc">==</span> <span class="st">&quot;Kosovo&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;XKX&quot;</span></span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>world <span class="sc">$</span> iso3c[ world <span class="sc">$</span> region <span class="sc">==</span> <span class="st">&quot;Micronesia&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;FSM&quot;</span></span></code></pre></div>
<p>Number of women per countries</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>fm_sum <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_name <span class="sc">!=</span> <span class="st">&quot;Unknown&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>  <span class="fu">select</span>(iso3c, fm_name, fm_gender) <span class="sc">|&gt;</span> </span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>  <span class="fu">unique</span>() <span class="sc">|&gt;</span> </span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>  <span class="fu">group_by</span>(iso3c) <span class="sc">|&gt;</span> </span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_ministers =</span> <span class="fu">n</span>(), </span>
<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a>            <span class="at">n_women =</span> <span class="fu">sum</span>(fm_gender)) <span class="sc">|&gt;</span> </span>
<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a>  <span class="fu">ungroup</span>()</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<pre><code>## select: dropped 127 variables (date, fwfm72_st, fwfm72_d, fwfm72_t, fwfm72_t0, …)</code></pre>
<pre><code>## group_by: one grouping variable (iso3c)</code></pre>
<pre><code>## summarise: now 202 rows and 3 columns, ungrouped</code></pre>
<pre><code>## ungroup: no grouping variables</code></pre>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">table</span>(fm_sum <span class="sc">$</span> n_women)</span></code></pre></div>
<pre><code>## 
##   0   1   2   3   5 
## 125  46  24   6   1</code></pre>
<p>Collapse the categories for 3+</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>fm_sum <span class="sc">|&gt;</span> <span class="fu">filter</span>(n_women <span class="sc">&gt;=</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>## filter: removed 195 rows (97%), 7 rows remaining</code></pre>
<pre><code>## # A tibble: 7 × 3
##   iso3c n_ministers n_women
##   &lt;chr&gt;       &lt;int&gt;   &lt;dbl&gt;
## 1 GTM            27       5
## 2 KGZ            16       3
## 3 LTU            16       3
## 4 MDA            10       3
## 5 NGA            23       3
## 6 POL            20       3
## 7 TLS             4       3</code></pre>
<p>Create a character vector for the number of women.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a>fm_sum <span class="ot">&lt;-</span> fm_sum <span class="sc">|&gt;</span> </span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_women_lab =</span> <span class="fu">as.character</span>(n_women), </span>
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>         <span class="at">n_women_lab =</span> <span class="fu">if_else</span>(n_women <span class="sc">&gt;=</span> <span class="dv">3</span>, <span class="st">&quot;3+&quot;</span>, n_women_lab))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;n_women_lab&#39; (character) with 4 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="fu">with</span>(fm_sum, <span class="fu">table</span>(n_women_lab, n_women, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##            n_women
## n_women_lab   0   1   2   3   5
##          0  125   0   0   0   0
##          1    0  46   0   0   0
##          2    0   0  24   0   0
##          3+   0   0   0   6   1</code></pre>
<p>Join this data set with the map data.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>fm_map <span class="ot">&lt;-</span> <span class="fu">full_join</span>(world, fm_sum)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(iso3c)`
## full_join: added 3 columns (n_ministers, n_women, n_women_lab)
## &gt; rows only in x 8,868
## &gt; rows only in y 5
## &gt; matched rows 90,470
## &gt; ========
## &gt; rows total 99,343</code></pre>
<p>Check missing regions.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a>fm_map <span class="sc">|&gt;</span> <span class="fu">filter</span>(<span class="fu">is.na</span>(long)) <span class="sc">|&gt;</span> <span class="fu">pull</span>(iso3c)</span></code></pre></div>
<pre><code>## filter: removed 99,338 rows (&gt;99%), 5 rows remaining</code></pre>
<pre><code>## [1] &quot;CSK&quot; &quot;HKG&quot; &quot;MAC&quot; &quot;TUV&quot; &quot;YUG&quot;</code></pre>
<p>Hong Kong (HKG), Macao (MAC), and Tuvalu (TUV) are too small to see
on the map anyway. Chechoslovakia (CSK) and Yugoslavia (YUG) are
missing, as they are replaced by their successor countries.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="do">## Convert NA to 0</span></span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a>fm_map <span class="ot">&lt;-</span> fm_map <span class="sc">|&gt;</span> </span>
<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_women_lab =</span> <span class="fu">replace_na</span>(n_women_lab, <span class="st">&quot;0&quot;</span>))</span></code></pre></div>
<pre><code>## mutate: changed 8,868 values (9%) of &#39;n_women_lab&#39; (8868 fewer NA)</code></pre>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">with</span>(fm_map, <span class="fu">table</span>(n_women_lab, n_women, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##            n_women
## n_women_lab     0     1     2     3     5  &lt;NA&gt;
##          0  61447     0     0     0     0  8868
##          1      0 19739     0     0     0     0
##          2      0     0  7732     0     0     0
##          3+     0     0     0  1442   115     0</code></pre>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a>fm_map <span class="sc">|&gt;</span> </span>
<span id="cb32-2"><a href="#cb32-2" tabindex="-1"></a>  <span class="fu">filter</span>(region <span class="sc">!=</span> <span class="st">&quot;Antarctica&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb32-3"><a href="#cb32-3" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> long, <span class="at">y =</span> lat, <span class="at">group =</span> group, <span class="at">fill =</span> n_women_lab)) <span class="sc">+</span> </span>
<span id="cb32-4"><a href="#cb32-4" tabindex="-1"></a>  <span class="fu">geom_polygon</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>, <span class="at">size =</span> <span class="fl">0.1</span>) <span class="sc">+</span> </span>
<span id="cb32-5"><a href="#cb32-5" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb32-6"><a href="#cb32-6" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="fu">c</span>(<span class="fl">0.6</span>, <span class="fl">0.05</span>),</span>
<span id="cb32-7"><a href="#cb32-7" tabindex="-1"></a>        <span class="at">legend.direction =</span> <span class="st">&quot;horizontal&quot;</span>,</span>
<span id="cb32-8"><a href="#cb32-8" tabindex="-1"></a>        <span class="at">legend.background =</span> <span class="fu">element_rect</span>(<span class="at">fill =</span> <span class="st">&quot;white&quot;</span>, <span class="at">color=</span><span class="st">&quot;white&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb32-9"><a href="#cb32-9" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">fill =</span> <span class="st">&quot;Number of woman</span><span class="sc">\n</span><span class="st">finance ministers&quot;</span>) <span class="sc">+</span> </span>
<span id="cb32-10"><a href="#cb32-10" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">labels=</span><span class="cn">NULL</span>) <span class="sc">+</span> <span class="fu">scale_y_discrete</span>(<span class="at">labels=</span><span class="cn">NULL</span>) <span class="sc">+</span> </span>
<span id="cb32-11"><a href="#cb32-11" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;gray95&quot;</span>, <span class="st">&quot;gray65&quot;</span>, <span class="st">&quot;gray30&quot;</span>, <span class="st">&quot;black&quot;</span>))</span></code></pre></div>
<pre><code>## filter: removed 4,663 rows (5%), 94,680 rows remaining</code></pre>
<pre><code>## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/Figure1_Map.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">10</span>, <span class="at">height =</span> <span class="dv">5</span>)</span></code></pre></div>
</div>
<div id="first-women-ministers" class="section level2">
<h2>First Women Ministers</h2>
<p>The code below shows the numbers we report in the main text (in the
section titled “Testing the Effect of Financial Crises on Women’s
Entrance into Power”).</p>
<p>Number of countries that had seen a woman finance minister between
1972 and 2017.</p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a><span class="fu">with</span>(fm_sum, <span class="fu">table</span>(n_women <span class="sc">&gt;</span> <span class="dv">0</span>))</span></code></pre></div>
<pre><code>## 
## FALSE  TRUE 
##   125    77</code></pre>
<p>77 (out of 202) countries had at least one woman finance
ministers.</p>
<p>Identify countries with more than 1 women finance ministers.</p>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" tabindex="-1"></a>multi.ffm <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb38-2"><a href="#cb38-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb38-3"><a href="#cb38-3" tabindex="-1"></a>  <span class="fu">select</span>(country_name, iso3c, fm_name) <span class="sc">|&gt;</span> </span>
<span id="cb38-4"><a href="#cb38-4" tabindex="-1"></a>  <span class="fu">unique</span>() <span class="sc">|&gt;</span> </span>
<span id="cb38-5"><a href="#cb38-5" tabindex="-1"></a>  <span class="fu">group_by</span>(country_name, iso3c) <span class="sc">|&gt;</span> </span>
<span id="cb38-6"><a href="#cb38-6" tabindex="-1"></a>  <span class="fu">tally</span>()</span></code></pre></div>
<pre><code>## filter: removed 96,570 rows (96%), 3,942 rows remaining</code></pre>
<pre><code>## select: dropped 127 variables (date, fwfm72_st, fwfm72_d, fwfm72_t, fwfm72_t0, …)</code></pre>
<pre><code>## group_by: 2 grouping variables (country_name, iso3c)</code></pre>
<pre><code>## tally: now 77 rows and 3 columns, one group variable remaining (country_name)</code></pre>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="do">## Number of countries that have 1 woman finance ministers.</span></span>
<span id="cb43-2"><a href="#cb43-2" tabindex="-1"></a>multi.ffm <span class="sc">|&gt;</span> <span class="fu">filter</span>(n <span class="sc">&gt;</span> <span class="dv">1</span>)</span></code></pre></div>
<pre><code>## filter (grouped): removed 46 rows (60%), 31 rows remaining</code></pre>
<pre><code>## # A tibble: 31 × 3
## # Groups:   country_name [31]
##    country_name iso3c     n
##    &lt;chr&gt;        &lt;chr&gt; &lt;int&gt;
##  1 Andorra      AND       2
##  2 Aruba        ABW       2
##  3 Burkina Faso BFA       2
##  4 Burundi      BDI       2
##  5 Cuba         CUB       2
##  6 East Timor   TLS       3
##  7 Ecuador      ECU       2
##  8 Guatemala    GTM       5
##  9 Haiti        HTI       2
## 10 Honduras     HND       2
## # ℹ 21 more rows</code></pre>
<p>Those that have 2, 3, or 5</p>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" tabindex="-1"></a>multi.ffm <span class="sc">|&gt;</span> <span class="fu">filter</span>(n<span class="sc">==</span><span class="dv">2</span>) <span class="sc">|&gt;</span> <span class="fu">print</span>(<span class="at">n=</span><span class="cn">Inf</span>) <span class="co"># n = 2. 24</span></span></code></pre></div>
<pre><code>## filter (grouped): removed 53 rows (69%), 24 rows remaining</code></pre>
<pre><code>## # A tibble: 24 × 3
## # Groups:   country_name [24]
##    country_name          iso3c     n
##    &lt;chr&gt;                 &lt;chr&gt; &lt;int&gt;
##  1 Andorra               AND       2
##  2 Aruba                 ABW       2
##  3 Burkina Faso          BFA       2
##  4 Burundi               BDI       2
##  5 Cuba                  CUB       2
##  6 Ecuador               ECU       2
##  7 Haiti                 HTI       2
##  8 Honduras              HND       2
##  9 Latvia                LVA       2
## 10 Liberia               LBR       2
## 11 Mali                  MLI       2
## 12 Norway                NOR       2
## 13 Peru                  PER       2
## 14 Portugal              PRT       2
## 15 Romania               ROU       2
## 16 Sao Tome and Principe STP       2
## 17 Suriname              SUR       2
## 18 Sweden                SWE       2
## 19 Switzerland           CHE       2
## 20 Taiwan                TWN       2
## 21 Tanzania              TZA       2
## 22 Turkmenistan          TKM       2
## 23 Uganda                UGA       2
## 24 Venezuela             VEN       2</code></pre>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" tabindex="-1"></a>multi.ffm <span class="sc">|&gt;</span> <span class="fu">filter</span>(n<span class="sc">==</span><span class="dv">3</span>) <span class="sc">|&gt;</span> <span class="fu">print</span>(<span class="at">n=</span><span class="cn">Inf</span>) <span class="co"># n = 3. 6</span></span></code></pre></div>
<pre><code>## filter (grouped): removed 71 rows (92%), 6 rows remaining</code></pre>
<pre><code>## # A tibble: 6 × 3
## # Groups:   country_name [6]
##   country_name iso3c     n
##   &lt;chr&gt;        &lt;chr&gt; &lt;int&gt;
## 1 East Timor   TLS       3
## 2 Kyrgyzstan   KGZ       3
## 3 Lithuania    LTU       3
## 4 Moldova      MDA       3
## 5 Nigeria      NGA       3
## 6 Poland       POL       3</code></pre>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" tabindex="-1"></a>multi.ffm <span class="sc">|&gt;</span> <span class="fu">filter</span>(n<span class="sc">&gt;</span><span class="dv">3</span>) <span class="sc">|&gt;</span> <span class="fu">print</span>(<span class="at">n=</span><span class="cn">Inf</span>) <span class="co"># n &gt; 3. 1</span></span></code></pre></div>
<pre><code>## filter (grouped): removed 76 rows (99%), one row remaining</code></pre>
<pre><code>## # A tibble: 1 × 3
## # Groups:   country_name [1]
##   country_name iso3c     n
##   &lt;chr&gt;        &lt;chr&gt; &lt;int&gt;
## 1 Guatemala    GTM       5</code></pre>
<p>Listing up the women finance ministers:</p>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> </span>
<span id="cb55-2"><a href="#cb55-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb55-3"><a href="#cb55-3" tabindex="-1"></a>  <span class="fu">group_by</span>(country_name, fm_name) <span class="sc">|&gt;</span> </span>
<span id="cb55-4"><a href="#cb55-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">first_date =</span> <span class="fu">min</span>(date)) <span class="sc">|&gt;</span> </span>
<span id="cb55-5"><a href="#cb55-5" tabindex="-1"></a>  <span class="fu">arrange</span>(first_date)</span></code></pre></div>
<pre><code>## filter: removed 96,570 rows (96%), 3,942 rows remaining</code></pre>
<pre><code>## group_by: 2 grouping variables (country_name, fm_name)</code></pre>
<pre><code>## summarise: now 117 rows and 3 columns, one group variable remaining (country_name)</code></pre>
<pre><code>## # A tibble: 117 × 3
## # Groups:   country_name [77]
##    country_name             fm_name                 first_date
##    &lt;chr&gt;                    &lt;chr&gt;                   &lt;date&gt;    
##  1 Panama                   Dora Reluz              1972-07-01
##  2 Central African Republic Marie-Christine Gboukou 1975-06-01
##  3 Liberia                  Ellen Sirleaf Johnson   1979-07-01
##  4 Korea, North             Ki-chong Yun            1980-04-01
##  5 Dominica                 Mary Eugenia Charles    1980-07-01
##  6 San Marino               Clara Boscaglia         1986-01-01
##  7 Burkina Faso             Bintou Sanogoh          1988-04-01
##  8 Taiwan                   Shirley Kuo             1988-07-01
##  9 Pakistan                 Benazir Bhutto          1989-01-01
## 10 Venezuela                Eglee Iturbe de Blanco  1989-03-01
## # ℹ 107 more rows</code></pre>
<p>The first one is Dora Reluz from Panama in July 1972.</p>
<p>87 out of 117 were appointed after 2000</p>
<div class="sourceCode" id="cb60"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> </span>
<span id="cb60-2"><a href="#cb60-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb60-3"><a href="#cb60-3" tabindex="-1"></a>  <span class="fu">group_by</span>(country_name, fm_name) <span class="sc">|&gt;</span> </span>
<span id="cb60-4"><a href="#cb60-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">first_date =</span> <span class="fu">min</span>(date)) <span class="sc">|&gt;</span> </span>
<span id="cb60-5"><a href="#cb60-5" tabindex="-1"></a>  <span class="fu">filter</span>(first_date <span class="sc">&gt;=</span> <span class="st">&quot;2000-01-01&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb60-6"><a href="#cb60-6" tabindex="-1"></a>  <span class="fu">arrange</span>(first_date)</span></code></pre></div>
<pre><code>## filter: removed 96,570 rows (96%), 3,942 rows remaining</code></pre>
<pre><code>## group_by: 2 grouping variables (country_name, fm_name)</code></pre>
<pre><code>## summarise: now 117 rows and 3 columns, one group variable remaining (country_name)</code></pre>
<pre><code>## filter (grouped): removed 30 rows (26%), 87 rows remaining</code></pre>
<pre><code>## # A tibble: 87 × 3
## # Groups:   country_name [63]
##    country_name          fm_name                       first_date
##    &lt;chr&gt;                 &lt;chr&gt;                         &lt;date&gt;    
##  1 Mozambique            Luisa Diogo                   2000-02-01
##  2 Denmark               Pia Gjellerup                 2001-01-01
##  3 Andorra               Mireia Maestre                2001-04-01
##  4 Lithuania             Dalia Grybauskaite            2001-07-01
##  5 Poland                Halina Wasilewska-Trenkner    2001-09-01
##  6 Turkmenistan          Geldiyevna Atayeva            2001-11-01
##  7 Moldova               Zinaida Greceanii             2002-02-01
##  8 Sao Tome and Principe Maria Santos Tebus            2002-02-01
##  9 East Timor            Maria Madalena Brites Boavida 2002-05-01
## 10 Portugal              Manuela Ferreira Leite        2002-05-01
## # ℹ 77 more rows</code></pre>
</div>
<div id="tally-by-regions" class="section level2">
<h2>Tally by Regions</h2>
<p>We define regions based on COW (GW) code.</p>
<div class="sourceCode" id="cb66"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb66-1"><a href="#cb66-1" tabindex="-1"></a>region_cut  <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">200</span>, <span class="dv">400</span>, <span class="dv">600</span>, <span class="dv">700</span>)</span>
<span id="cb66-2"><a href="#cb66-2" tabindex="-1"></a>region_name <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Americas&quot;</span>, <span class="st">&quot;Europe&quot;</span>, <span class="st">&quot;Africa&quot;</span>, <span class="st">&quot;Middle East&quot;</span>, <span class="st">&quot;Asia-Pacific&quot;</span>)</span>
<span id="cb66-3"><a href="#cb66-3" tabindex="-1"></a></span>
<span id="cb66-4"><a href="#cb66-4" tabindex="-1"></a><span class="do">## Define regions</span></span>
<span id="cb66-5"><a href="#cb66-5" tabindex="-1"></a>df <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb66-6"><a href="#cb66-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">COWregion =</span> region_name[<span class="fu">findInterval</span>(gwcode, region_cut)])</span></code></pre></div>
<pre><code>## mutate: new variable &#39;COWregion&#39; (character) with 6 unique values and 1% NA</code></pre>
<div class="sourceCode" id="cb68"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" tabindex="-1"></a><span class="do">## Check missing</span></span>
<span id="cb68-2"><a href="#cb68-2" tabindex="-1"></a><span class="fu">table</span>(df <span class="sc">$</span> COWregion, <span class="at">useNA =</span> <span class="st">&quot;always&quot;</span>)</span></code></pre></div>
<pre><code>## 
##       Africa     Americas Asia-Pacific       Europe  Middle East         &lt;NA&gt; 
##        25107        18595        21592        22629        11118         1471</code></pre>
<p>Some values were not matched unambiguously: ABW, BMU, COK, HKG, MAC,
SRB, SVU, XKX, YUG</p>
<div class="sourceCode" id="cb70"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb70-1"><a href="#cb70-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> </span>
<span id="cb70-2"><a href="#cb70-2" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">is.na</span>(gwcode) <span class="sc">==</span> T) <span class="sc">|&gt;</span> <span class="fu">pull</span>(country_name) <span class="sc">|&gt;</span> <span class="fu">unique</span>()</span></code></pre></div>
<pre><code>## filter: removed 99,041 rows (99%), 1,471 rows remaining</code></pre>
<pre><code>## [1] &quot;Aruba&quot;            &quot;Bermuda&quot;          &quot;Cook Islands&quot;     &quot;China- Hong Kong&quot;
## [5] &quot;China- Macau&quot;</code></pre>
<p>Fill in NAs manually.</p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" tabindex="-1"></a>df <span class="sc">$</span> COWregion[df <span class="sc">$</span> country_name <span class="sc">==</span> <span class="st">&quot;Aruba&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Europe&quot;</span>　<span class="co"># Part of Netherlands / Geographically in the Americas</span></span>
<span id="cb73-2"><a href="#cb73-2" tabindex="-1"></a>df <span class="sc">$</span> COWregion[df <span class="sc">$</span> country_name <span class="sc">==</span> <span class="st">&quot;Bermuda&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Europe&quot;</span>　<span class="co"># UK&#39;s overseas territory / Geographically in Americas?</span></span>
<span id="cb73-3"><a href="#cb73-3" tabindex="-1"></a>df <span class="sc">$</span> COWregion[df <span class="sc">$</span> country_name <span class="sc">==</span> <span class="st">&quot;Cook Islands&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Asia-Pacific&quot;</span>　<span class="co"># Part of NZ</span></span>
<span id="cb73-4"><a href="#cb73-4" tabindex="-1"></a>df <span class="sc">$</span> COWregion[df <span class="sc">$</span> country_name <span class="sc">==</span> <span class="st">&quot;China- Hong Kong&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Asia-Pacific&quot;</span></span>
<span id="cb73-5"><a href="#cb73-5" tabindex="-1"></a>df <span class="sc">$</span> COWregion[df <span class="sc">$</span> country_name <span class="sc">==</span> <span class="st">&quot;China- Macau&quot;</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Asia-Pacific&quot;</span></span></code></pre></div>
<p>Tally by region</p>
<div class="sourceCode" id="cb74"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> </span>
<span id="cb74-2"><a href="#cb74-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender<span class="sc">==</span><span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb74-3"><a href="#cb74-3" tabindex="-1"></a>  <span class="fu">select</span>(COWregion, iso3c) <span class="sc">|&gt;</span> </span>
<span id="cb74-4"><a href="#cb74-4" tabindex="-1"></a>  <span class="fu">unique</span>() <span class="sc">|&gt;</span> </span>
<span id="cb74-5"><a href="#cb74-5" tabindex="-1"></a>  <span class="fu">group_by</span>(COWregion) <span class="sc">|&gt;</span> <span class="fu">tally</span>()</span></code></pre></div>
<pre><code>## filter: removed 96,570 rows (96%), 3,942 rows remaining</code></pre>
<pre><code>## select: dropped 129 variables (date, fwfm72_st, fwfm72_d, fwfm72_t, fwfm72_t0, …)</code></pre>
<pre><code>## group_by: one grouping variable (COWregion)</code></pre>
<pre><code>## tally: now 5 rows and 2 columns, ungrouped</code></pre>
<pre><code>## # A tibble: 5 × 2
##   COWregion        n
##   &lt;chr&gt;        &lt;int&gt;
## 1 Africa          19
## 2 Americas        16
## 3 Asia-Pacific    11
## 4 Europe          29
## 5 Middle East      2</code></pre>
<p>We can see that women have been appointed to the finance portfolio in
every region of the world.</p>
</div>
<div id="minister-level-statistics" class="section level2">
<h2>Minister-Level Statistics</h2>
<p>Calculate minister-level summaries that we mention in the text.</p>
<div class="sourceCode" id="cb80"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb80-1"><a href="#cb80-1" tabindex="-1"></a>min_lev <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb80-2"><a href="#cb80-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_name <span class="sc">!=</span> <span class="st">&quot;Unknown&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb80-3"><a href="#cb80-3" tabindex="-1"></a>  <span class="fu">group_by</span>(iso3c, fm_name) <span class="sc">|&gt;</span> </span>
<span id="cb80-4"><a href="#cb80-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fm_gender =</span> <span class="fu">mean</span>(fm_gender))</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<pre><code>## group_by: 2 grouping variables (iso3c, fm_name)</code></pre>
<pre><code>## summarise: now 2,976 rows and 3 columns, one group variable remaining (iso3c)</code></pre>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" tabindex="-1"></a><span class="fu">table</span>(min_lev <span class="sc">$</span> fm_gender)  </span></code></pre></div>
<pre><code>## 
##    0    1 
## 2859  117</code></pre>
<p>We have 2859 + 117 = 2976 unique ministers, of which 117 (4%) are
women.</p>
<p>Spell-level summaries</p>
<div class="sourceCode" id="cb86"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb86-1"><a href="#cb86-1" tabindex="-1"></a>spell_lev <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb86-2"><a href="#cb86-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_name <span class="sc">!=</span> <span class="st">&quot;Unknown&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb86-3"><a href="#cb86-3" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb86-4"><a href="#cb86-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">time =</span> <span class="fu">max</span>(tenure_t), </span>
<span id="cb86-5"><a href="#cb86-5" tabindex="-1"></a>            <span class="at">fail =</span> <span class="fu">max</span>(tenure_d), </span>
<span id="cb86-6"><a href="#cb86-6" tabindex="-1"></a>            <span class="at">decade =</span> <span class="fu">max</span>(decade), <span class="co"># decade of termination</span></span>
<span id="cb86-7"><a href="#cb86-7" tabindex="-1"></a>            <span class="at">year =</span> <span class="fu">max</span>(year),     <span class="co"># year of termination</span></span>
<span id="cb86-8"><a href="#cb86-8" tabindex="-1"></a>            <span class="at">decade0 =</span> <span class="fu">min</span>(decade),    <span class="co"># decade of appointment</span></span>
<span id="cb86-9"><a href="#cb86-9" tabindex="-1"></a>            <span class="at">year0 =</span> <span class="fu">min</span>(year),    <span class="co"># year of appointment</span></span>
<span id="cb86-10"><a href="#cb86-10" tabindex="-1"></a>            <span class="at">fm_gender =</span> <span class="fu">mean</span>(fm_gender), </span>
<span id="cb86-11"><a href="#cb86-11" tabindex="-1"></a>            <span class="at">fm_gender_f =</span> <span class="fu">if_else</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Woman&quot;</span>, <span class="st">&quot;Man&quot;</span>), </span>
<span id="cb86-12"><a href="#cb86-12" tabindex="-1"></a>            <span class="at">fm_name =</span> <span class="fu">first</span>(fm_name),</span>
<span id="cb86-13"><a href="#cb86-13" tabindex="-1"></a>            <span class="at">country_name =</span> <span class="fu">first</span>(country_name),</span>
<span id="cb86-14"><a href="#cb86-14" tabindex="-1"></a>            <span class="at">left_trunc =</span> <span class="fu">min</span>(left_trunc),</span>
<span id="cb86-15"><a href="#cb86-15" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb86-16"><a href="#cb86-16" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 3,257 rows and 13 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb90"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb90-1"><a href="#cb90-1" tabindex="-1"></a><span class="fu">table</span>(spell_lev <span class="sc">$</span> fm_gender_f)</span></code></pre></div>
<pre><code>## 
##   Man Woman 
##  3134   123</code></pre>
<p>Which women served multiple times?</p>
<div class="sourceCode" id="cb92"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb92-1"><a href="#cb92-1" tabindex="-1"></a>spell_lev <span class="sc">|&gt;</span> </span>
<span id="cb92-2"><a href="#cb92-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender_f <span class="sc">==</span> <span class="st">&quot;Woman&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb92-3"><a href="#cb92-3" tabindex="-1"></a>  <span class="fu">group_by</span>(fm_name, country_name) <span class="sc">|&gt;</span> </span>
<span id="cb92-4"><a href="#cb92-4" tabindex="-1"></a>  <span class="fu">tally</span>() <span class="sc">|&gt;</span> </span>
<span id="cb92-5"><a href="#cb92-5" tabindex="-1"></a>  <span class="fu">filter</span>(n <span class="sc">&gt;</span> <span class="dv">1</span>)</span></code></pre></div>
<pre><code>## filter: removed 3,134 rows (96%), 123 rows remaining</code></pre>
<pre><code>## group_by: 2 grouping variables (fm_name, country_name)</code></pre>
<pre><code>## tally: now 117 rows and 3 columns, one group variable remaining (fm_name)</code></pre>
<pre><code>## filter (grouped): removed 112 rows (96%), 5 rows remaining</code></pre>
<pre><code>## # A tibble: 5 × 3
## # Groups:   fm_name [5]
##   fm_name                   country_name     n
##   &lt;chr&gt;                     &lt;chr&gt;        &lt;int&gt;
## 1 Benazir Bhutto            Pakistan         2
## 2 Ellen Sirleaf Johnson     Liberia          2
## 3 Marie Carmelle Jean-Marie Haiti            2
## 4 Ngozi Okonjo-Iweala       Nigeria          3
## 5 Sri Mulyani Indrawati     Indonesia        2</code></pre>
</div>
<div id="minister-tenure-by-gender-appendix-a2" class="section level2">
<h2>Minister Tenure by Gender (Appendix A2)</h2>
<p>Left truncation and right censoring</p>
<div class="sourceCode" id="cb98"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb98-1"><a href="#cb98-1" tabindex="-1"></a><span class="fu">with</span>(spell_lev, <span class="fu">table</span>(left_trunc, right_cens_max, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##           right_cens_max
## left_trunc    0    1
##          0 2898  200
##          1  157    2</code></pre>
<div class="sourceCode" id="cb100"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb100-1"><a href="#cb100-1" tabindex="-1"></a>spell_lev <span class="sc">|&gt;</span> <span class="fu">filter</span>(left_trunc <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> right_cens_max <span class="sc">==</span> <span class="dv">1</span>)</span></code></pre></div>
<pre><code>## filter: removed 3,255 rows (&gt;99%), 2 rows remaining</code></pre>
<pre><code>## # A tibble: 2 × 13
##   spellid  time  fail decade  year decade0 year0 fm_gender fm_gender_f fm_name  
##     &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;   &lt;dbl&gt; &lt;dbl&gt;     &lt;dbl&gt; &lt;chr&gt;       &lt;chr&gt;    
## 1      77  554.     0   2010  2017    2010  2017         0 Man         Hamdan b…
## 2    2228  578.     0   2010  2017    2010  2017         0 Man         Qaboos S…
## # ℹ 3 more variables: country_name &lt;chr&gt;, left_trunc &lt;dbl&gt;,
## #   right_cens_max &lt;dbl&gt;</code></pre>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" tabindex="-1"></a>spell_lev <span class="sc">|&gt;</span> <span class="fu">filter</span>(right_cens_max <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> <span class="fu">pull</span>(fm_gender_f) <span class="sc">|&gt;</span> <span class="fu">table</span>()</span></code></pre></div>
<pre><code>## filter: removed 3,055 rows (94%), 202 rows remaining</code></pre>
<pre><code>## 
##   Man Woman 
##   190    12</code></pre>
<p>We will focus on non-censored spells. We can keep the left-truncated
spells because we have records of the origin dates for them.</p>
<div class="sourceCode" id="cb106"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb106-1"><a href="#cb106-1" tabindex="-1"></a>spell_full <span class="ot">&lt;-</span> spell_lev <span class="sc">|&gt;</span> <span class="fu">filter</span>(right_cens_max <span class="sc">==</span> <span class="dv">0</span>)</span></code></pre></div>
<pre><code>## filter: removed 202 rows (6%), 3,055 rows remaining</code></pre>
<p>Spells that include right-censored ones.</p>
<div class="sourceCode" id="cb108"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb108-1"><a href="#cb108-1" tabindex="-1"></a><span class="fu">table</span>(spell_lev <span class="sc">$</span> fm_gender_f)</span></code></pre></div>
<pre><code>## 
##   Man Woman 
##  3134   123</code></pre>
<p>Spells that exclude right-censored ones.</p>
<div class="sourceCode" id="cb110"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb110-1"><a href="#cb110-1" tabindex="-1"></a><span class="fu">table</span>(spell_full <span class="sc">$</span> fm_gender_f)</span></code></pre></div>
<pre><code>## 
##   Man Woman 
##  2944   111</code></pre>
<p>Average tenure length by gender.</p>
<div class="sourceCode" id="cb112"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb112-1"><a href="#cb112-1" tabindex="-1"></a>spell_full <span class="sc">|&gt;</span> </span>
<span id="cb112-2"><a href="#cb112-2" tabindex="-1"></a>  <span class="fu">filter</span>(fail <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb112-3"><a href="#cb112-3" tabindex="-1"></a>  <span class="fu">group_by</span>(fm_gender_f) <span class="sc">|&gt;</span> </span>
<span id="cb112-4"><a href="#cb112-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">avg_tenure =</span> <span class="fu">median</span>(time))</span></code></pre></div>
<pre><code>## filter: no rows removed</code></pre>
<pre><code>## group_by: one grouping variable (fm_gender_f)</code></pre>
<pre><code>## summarise: now 2 rows and 2 columns, ungrouped</code></pre>
<pre><code>## # A tibble: 2 × 2
##   fm_gender_f avg_tenure
##   &lt;chr&gt;            &lt;dbl&gt;
## 1 Man               22.0
## 2 Woman             24.0</code></pre>
<p>Table A2: Minister Tenure by Gender, 1972-2017</p>
<div class="sourceCode" id="cb117"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb117-1"><a href="#cb117-1" tabindex="-1"></a>spell_stat <span class="ot">&lt;-</span> spell_full <span class="sc">|&gt;</span> </span>
<span id="cb117-2"><a href="#cb117-2" tabindex="-1"></a>  <span class="fu">group_by</span>(fm_gender_f) <span class="sc">|&gt;</span> </span>
<span id="cb117-3"><a href="#cb117-3" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n =</span> <span class="fu">n</span>(), </span>
<span id="cb117-4"><a href="#cb117-4" tabindex="-1"></a>            <span class="at">Mean =</span> <span class="fu">mean</span>(time) <span class="sc">|&gt;</span> <span class="fu">round</span>(<span class="dv">1</span>), </span>
<span id="cb117-5"><a href="#cb117-5" tabindex="-1"></a>            <span class="at">Median =</span> <span class="fu">median</span>(time) <span class="sc">|&gt;</span> <span class="fu">round</span>(<span class="dv">1</span>), </span>
<span id="cb117-6"><a href="#cb117-6" tabindex="-1"></a>            <span class="at">Minimum =</span> <span class="fu">min</span>(time) <span class="sc">|&gt;</span> <span class="fu">round</span>(<span class="dv">1</span>), </span>
<span id="cb117-7"><a href="#cb117-7" tabindex="-1"></a>            <span class="at">Maximum =</span> <span class="fu">max</span>(time) <span class="sc">|&gt;</span> <span class="fu">round</span>(<span class="dv">1</span>), </span>
<span id="cb117-8"><a href="#cb117-8" tabindex="-1"></a>            <span class="at">SD =</span> <span class="fu">sd</span>(time) <span class="sc">|&gt;</span> <span class="fu">round</span>(<span class="dv">1</span>)) <span class="sc">|&gt;</span> </span>
<span id="cb117-9"><a href="#cb117-9" tabindex="-1"></a>  <span class="fu">as.data.frame</span>()</span></code></pre></div>
<pre><code>## group_by: one grouping variable (fm_gender_f)</code></pre>
<pre><code>## summarise: now 2 rows and 7 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb120"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb120-1"><a href="#cb120-1" tabindex="-1"></a><span class="fu">kable</span>(spell_stat)</span></code></pre></div>
<table>
<thead>
<tr>
<th style="text-align:left;">
fm_gender_f
</th>
<th style="text-align:right;">
n
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
Median
</th>
<th style="text-align:right;">
Minimum
</th>
<th style="text-align:right;">
Maximum
</th>
<th style="text-align:right;">
SD
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Man
</td>
<td style="text-align:right;">
2944
</td>
<td style="text-align:right;">
31.9
</td>
<td style="text-align:right;">
22
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
385.5
</td>
<td style="text-align:right;">
33.8
</td>
</tr>
<tr>
<td style="text-align:left;">
Woman
</td>
<td style="text-align:right;">
111
</td>
<td style="text-align:right;">
32.6
</td>
<td style="text-align:right;">
24
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
223.3
</td>
<td style="text-align:right;">
34.0
</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb121"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb121-1"><a href="#cb121-1" tabindex="-1"></a><span class="fu">summary</span>(spell_full <span class="sc">$</span> time[spell_full <span class="sc">$</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>])</span></code></pre></div>
<pre><code>##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.02   12.01   24.05   32.56   39.06  223.29</code></pre>
<div class="sourceCode" id="cb123"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb123-1"><a href="#cb123-1" tabindex="-1"></a><span class="fu">datatable</span>(spell_stat)</span></code></pre></div>
<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-4f6ec425a3bc656914eb" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-4f6ec425a3bc656914eb">{"x":{"filter":"none","vertical":false,"data":[["1","2"],["Man","Woman"],[2944,111],[31.9,32.6],[22,24],[1,1],[385.5,223.3],[33.8,34]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>fm_gender_f<\/th>\n      <th>n<\/th>\n      <th>Mean<\/th>\n      <th>Median<\/th>\n      <th>Minimum<\/th>\n      <th>Maximum<\/th>\n      <th>SD<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<p>Visualization</p>
<div class="sourceCode" id="cb124"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb124-1"><a href="#cb124-1" tabindex="-1"></a>g_tenure <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(spell_full, </span>
<span id="cb124-2"><a href="#cb124-2" tabindex="-1"></a>                   <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> fm_gender_f, <span class="at">y =</span> time, </span>
<span id="cb124-3"><a href="#cb124-3" tabindex="-1"></a>                                 <span class="at">color =</span> fm_gender_f, </span>
<span id="cb124-4"><a href="#cb124-4" tabindex="-1"></a>                                 <span class="at">fill =</span> fm_gender_f)) <span class="sc">+</span> </span>
<span id="cb124-5"><a href="#cb124-5" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> my_pal, <span class="at">guide =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb124-6"><a href="#cb124-6" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> my_pal, <span class="at">guide =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span> </span>
<span id="cb124-7"><a href="#cb124-7" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;&quot;</span>, <span class="at">y =</span> <span class="st">&quot;Minister tenure (months)&quot;</span>) <span class="sc">+</span> </span>
<span id="cb124-8"><a href="#cb124-8" tabindex="-1"></a>  <span class="fu">geom_boxplot</span>(</span>
<span id="cb124-9"><a href="#cb124-9" tabindex="-1"></a>    <span class="at">width =</span> .<span class="dv">2</span>, <span class="at">fill =</span> <span class="st">&quot;white&quot;</span>,</span>
<span id="cb124-10"><a href="#cb124-10" tabindex="-1"></a>    <span class="at">size =</span> <span class="fl">1.5</span>, <span class="at">outlier.shape =</span> <span class="cn">NA</span></span>
<span id="cb124-11"><a href="#cb124-11" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb124-12"><a href="#cb124-12" tabindex="-1"></a>  ggdist<span class="sc">::</span><span class="fu">stat_halfeye</span>(</span>
<span id="cb124-13"><a href="#cb124-13" tabindex="-1"></a>    <span class="at">adjust =</span> .<span class="dv">33</span>, <span class="do">## bandwidth</span></span>
<span id="cb124-14"><a href="#cb124-14" tabindex="-1"></a>    <span class="at">width =</span> .<span class="dv">67</span>, </span>
<span id="cb124-15"><a href="#cb124-15" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NA</span>, <span class="do">## remove slab interval</span></span>
<span id="cb124-16"><a href="#cb124-16" tabindex="-1"></a>    <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>)</span>
<span id="cb124-17"><a href="#cb124-17" tabindex="-1"></a>  ) <span class="sc">+</span>  </span>
<span id="cb124-18"><a href="#cb124-18" tabindex="-1"></a>  gghalves<span class="sc">::</span><span class="fu">geom_half_point</span>(</span>
<span id="cb124-19"><a href="#cb124-19" tabindex="-1"></a>    <span class="at">side =</span> <span class="st">&quot;l&quot;</span>, </span>
<span id="cb124-20"><a href="#cb124-20" tabindex="-1"></a>    <span class="at">range_scale =</span> .<span class="dv">3</span>, </span>
<span id="cb124-21"><a href="#cb124-21" tabindex="-1"></a>    <span class="at">alpha =</span> .<span class="dv">5</span>, <span class="at">size =</span> <span class="dv">3</span></span>
<span id="cb124-22"><a href="#cb124-22" tabindex="-1"></a>  ) <span class="sc">+</span> </span>
<span id="cb124-23"><a href="#cb124-23" tabindex="-1"></a>  <span class="fu">theme_minimal</span>()</span></code></pre></div>
<div class="sourceCode" id="cb125"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb125-1"><a href="#cb125-1" tabindex="-1"></a>g_tenure <span class="sc">+</span> <span class="fu">scale_y_log10</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb126"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb126-1"><a href="#cb126-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureA2_MinisterTenure.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">6</span>)</span></code></pre></div>
<p>Average tenure length by decades</p>
<div class="sourceCode" id="cb127"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb127-1"><a href="#cb127-1" tabindex="-1"></a>spell_full <span class="sc">|&gt;</span> </span>
<span id="cb127-2"><a href="#cb127-2" tabindex="-1"></a>  <span class="fu">group_by</span>(decade) <span class="sc">|&gt;</span> </span>
<span id="cb127-3"><a href="#cb127-3" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">avg_tenure =</span> <span class="fu">median</span>(time))</span></code></pre></div>
<pre><code>## group_by: one grouping variable (decade)</code></pre>
<pre><code>## summarise: now 5 rows and 2 columns, ungrouped</code></pre>
<pre><code>## # A tibble: 5 × 2
##   decade avg_tenure
##    &lt;dbl&gt;      &lt;dbl&gt;
## 1   1970       21.0
## 2   1980       24.0
## 3   1990       19.0
## 4   2000       23.1
## 5   2010       24.1</code></pre>
</div>
<div id="descriptive-statistics-table-a1" class="section level2">
<h2>Descriptive Statistics (Table A1)</h2>
<p>Code below produces a table that shows descriptive statistics for
covariates that are measured annually.</p>
<div class="sourceCode" id="cb131"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb131-1"><a href="#cb131-1" tabindex="-1"></a>covs_year <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb131-2"><a href="#cb131-2" tabindex="-1"></a>  <span class="fu">select</span>(iso3c, year, </span>
<span id="cb131-3"><a href="#cb131-3" tabindex="-1"></a>         womenrep_lag, leadgender, presidential, unified_aut, xm_qudsest, </span>
<span id="cb131-4"><a href="#cb131-4" tabindex="-1"></a>         inflate_lag, loggdppc_lag, growth_lag, tradepergdp_i_lag, kaopen_i_lag) <span class="sc">|&gt;</span> </span>
<span id="cb131-5"><a href="#cb131-5" tabindex="-1"></a>  <span class="fu">group_by</span>(iso3c, year) <span class="sc">|&gt;</span> </span>
<span id="cb131-6"><a href="#cb131-6" tabindex="-1"></a>  <span class="fu">summarise_all</span>(median, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<pre><code>## select: dropped 119 variables (date, fwfm72_st, fwfm72_d, fwfm72_t, fwfm72_t0, …)</code></pre>
<pre><code>## group_by: 2 grouping variables (iso3c, year)</code></pre>
<pre><code>## summarise_all: now 8,403 rows and 12 columns, one group variable remaining (iso3c)</code></pre>
<div class="sourceCode" id="cb135"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb135-1"><a href="#cb135-1" tabindex="-1"></a><span class="fu">stargazer</span>(covs_year <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="sc">-</span>iso3c, <span class="sc">-</span>year) <span class="sc">|&gt;</span> <span class="fu">as.data.frame</span>(), </span>
<span id="cb135-2"><a href="#cb135-2" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>, </span>
<span id="cb135-3"><a href="#cb135-3" tabindex="-1"></a>          <span class="at">digits =</span> <span class="dv">1</span>,</span>
<span id="cb135-4"><a href="#cb135-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Women in Parliament&quot;</span>, </span>
<span id="cb135-5"><a href="#cb135-5" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, </span>
<span id="cb135-6"><a href="#cb135-6" tabindex="-1"></a>                               <span class="st">&quot;Presidential System&quot;</span>, </span>
<span id="cb135-7"><a href="#cb135-7" tabindex="-1"></a>                               <span class="st">&quot;Unified Government&quot;</span>, </span>
<span id="cb135-8"><a href="#cb135-8" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, </span>
<span id="cb135-9"><a href="#cb135-9" tabindex="-1"></a>                               <span class="st">&quot;Inflation&quot;</span>, </span>
<span id="cb135-10"><a href="#cb135-10" tabindex="-1"></a>                               <span class="st">&quot;Per capita GDP (logged)&quot;</span>, </span>
<span id="cb135-11"><a href="#cb135-11" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, </span>
<span id="cb135-12"><a href="#cb135-12" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, </span>
<span id="cb135-13"><a href="#cb135-13" tabindex="-1"></a>                               <span class="st">&quot;Capital Openness&quot;</span>), </span>
<span id="cb135-14"><a href="#cb135-14" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table A1: Descriptive Statistics for Annually Measured Covariates, 1972-2017&quot;</span>,</span>
<span id="cb135-15"><a href="#cb135-15" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableA1.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table A1: Descriptive Statistics for Annually Measured
Covariates, 1972-2017</strong>
</caption>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Statistic
</td>
<td>
N
</td>
<td>
Mean
</td>
<td>
St. Dev.
</td>
<td>
Min
</td>
<td>
Max
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
6,784
</td>
<td>
12.0
</td>
<td>
10.2
</td>
<td>
0.0
</td>
<td>
63.8
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
8,403
</td>
<td>
0.04
</td>
<td>
0.2
</td>
<td>
0
</td>
<td>
1
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential System
</td>
<td>
8,035
</td>
<td>
0.6
</td>
<td>
0.5
</td>
<td>
0
</td>
<td>
1
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
7,060
</td>
<td>
0.6
</td>
<td>
0.5
</td>
<td>
0
</td>
<td>
1
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
7,395
</td>
<td>
0.5
</td>
<td>
0.9
</td>
<td>
-1.7
</td>
<td>
3.1
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
8,005
</td>
<td>
0.004
</td>
<td>
1.0
</td>
<td>
-0.2
</td>
<td>
60.8
</td>
</tr>
<tr>
<td style="text-align:left">
Per capita GDP (logged)
</td>
<td>
8,362
</td>
<td>
8.5
</td>
<td>
1.3
</td>
<td>
5.1
</td>
<td>
12.8
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
8,359
</td>
<td>
1.8
</td>
<td>
4.7
</td>
<td>
-44.7
</td>
<td>
82.7
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
7,493
</td>
<td>
81.4
</td>
<td>
57.0
</td>
<td>
0.02
</td>
<td>
442.6
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
7,475
</td>
<td>
-0.04
</td>
<td>
1.4
</td>
<td>
-2.0
</td>
<td>
2.0
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
</table>
</div>
<div id="gender-ratio-during-crisis-figure-a1" class="section level2">
<h2>Gender Ratio during Crisis (Figure A1)</h2>
<p>Crisis Measures</p>
<div class="sourceCode" id="cb136"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb136-1"><a href="#cb136-1" tabindex="-1"></a>df_crisis <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb136-2"><a href="#cb136-2" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">fm_gender_f =</span> <span class="fu">if_else</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Woman&quot;</span>, <span class="st">&quot;Man&quot;</span>),</span>
<span id="cb136-3"><a href="#cb136-3" tabindex="-1"></a>         <span class="at">crisis_f =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Crisis&quot;</span>, <span class="st">&quot;Not crisis&quot;</span>),</span>
<span id="cb136-4"><a href="#cb136-4" tabindex="-1"></a>         <span class="at">crisis_f_n =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">1</span>, </span>
<span id="cb136-5"><a href="#cb136-5" tabindex="-1"></a>                              <span class="st">&quot;Crisis (n = 5,111)&quot;</span>, </span>
<span id="cb136-6"><a href="#cb136-6" tabindex="-1"></a>                              <span class="st">&quot;Not crisis (n = 95,401)&quot;</span>))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;fm_gender_f&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis_f&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis_f_n&#39; (character) with 2 unique values and 0% NA</code></pre>
<p>Banking Crisis (monthly)</p>
<div class="sourceCode" id="cb140"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb140-1"><a href="#cb140-1" tabindex="-1"></a><span class="fu">table</span>(df_crisis <span class="sc">$</span> crisis_f_n, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>)</span></code></pre></div>
<pre><code>## 
##      Crisis (n = 5,111) Not crisis (n = 95,401) 
##                    5111                   95401</code></pre>
<div class="sourceCode" id="cb142"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb142-1"><a href="#cb142-1" tabindex="-1"></a><span class="fu">with</span>(df_crisis, <span class="fu">table</span>(crisis_f, fm_gender_f, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##             fm_gender_f
## crisis_f       Man Woman
##   Crisis      4843   268
##   Not crisis 91727  3674</code></pre>
<ul>
<li>Time since last non-crisis (during crisis)</li>
<li>Time since last crisis (during non-crisis)</li>
</ul>
<div class="sourceCode" id="cb144"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb144-1"><a href="#cb144-1" tabindex="-1"></a>df_spell <span class="ot">&lt;-</span> df_crisis <span class="sc">|&gt;</span> </span>
<span id="cb144-2"><a href="#cb144-2" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">non_crisis_mon =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;non_crisis_mon&#39; (double) with 2 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb146"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb146-1"><a href="#cb146-1" tabindex="-1"></a><span class="fu">with</span>(df_spell, <span class="fu">table</span>(crisis_mon, non_crisis_mon))</span></code></pre></div>
<pre><code>##           non_crisis_mon
## crisis_mon     0     1
##          0     0 95401
##          1  5111     0</code></pre>
<p>Calculate the number of months since…<br />
- last non-crisis month (= 1 during peace time, 1,2,3,… during
crisis)<br />
- last crisis month (= 1 during crisis, 1,2,3,… during peace time)</p>
<div class="sourceCode" id="cb148"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb148-1"><a href="#cb148-1" tabindex="-1"></a>spell1 <span class="ot">&lt;-</span> pltesim<span class="sc">::</span><span class="fu">btscs</span>(df_spell, <span class="st">&quot;non_crisis_mon&quot;</span>, <span class="st">&quot;date&quot;</span>, <span class="st">&quot;iso3c&quot;</span>)</span>
<span id="cb148-2"><a href="#cb148-2" tabindex="-1"></a>spell1 <span class="ot">&lt;-</span> spell1 <span class="sc">|&gt;</span> <span class="fu">rename</span>(<span class="at">time_since_0 =</span> spell_time)</span></code></pre></div>
<pre><code>## rename: renamed one variable (time_since_0)</code></pre>
<div class="sourceCode" id="cb150"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb150-1"><a href="#cb150-1" tabindex="-1"></a>spell2 <span class="ot">&lt;-</span> pltesim<span class="sc">::</span><span class="fu">btscs</span>(spell1, <span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;date&quot;</span>, <span class="st">&quot;iso3c&quot;</span>)</span>
<span id="cb150-2"><a href="#cb150-2" tabindex="-1"></a>df_spell <span class="ot">&lt;-</span> spell2 <span class="sc">|&gt;</span> <span class="fu">rename</span>(<span class="at">time_since_1 =</span> spell_time)</span></code></pre></div>
<pre><code>## rename: renamed one variable (time_since_1)</code></pre>
<p>Calculate the “risk” of woman holding FM-ship during a crisis.</p>
<div class="sourceCode" id="cb152"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb152-1"><a href="#cb152-1" tabindex="-1"></a>fmrisk_0 <span class="ot">&lt;-</span> df_spell <span class="sc">|&gt;</span> </span>
<span id="cb152-2"><a href="#cb152-2" tabindex="-1"></a>  <span class="fu">group_by</span>(crisis_mon, fm_gender) <span class="sc">|&gt;</span> </span>
<span id="cb152-3"><a href="#cb152-3" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">N =</span> <span class="fu">n</span>()) <span class="sc">|&gt;</span> </span>
<span id="cb152-4"><a href="#cb152-4" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Ratio =</span> <span class="fu">round</span>(N <span class="sc">/</span> <span class="fu">sum</span>(N), <span class="dv">4</span>))</span></code></pre></div>
<pre><code>## group_by: 2 grouping variables (crisis_mon, fm_gender)</code></pre>
<pre><code>## summarise: now 4 rows and 3 columns, one group variable remaining (crisis_mon)</code></pre>
<pre><code>## mutate (grouped): new variable &#39;Ratio&#39; (double) with 4 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb156"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb156-1"><a href="#cb156-1" tabindex="-1"></a>fmrisk_0</span></code></pre></div>
<pre><code>## # A tibble: 4 × 4
## # Groups:   crisis_mon [2]
##   crisis_mon fm_gender     N  Ratio
##        &lt;dbl&gt;     &lt;dbl&gt; &lt;int&gt;  &lt;dbl&gt;
## 1          0         0 91727 0.962 
## 2          0         1  3674 0.0385
## 3          1         0  4843 0.948 
## 4          1         1   268 0.0524</code></pre>
<div class="sourceCode" id="cb158"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb158-1"><a href="#cb158-1" tabindex="-1"></a>fmrisk_t0 <span class="ot">&lt;-</span> fmrisk_0 <span class="sc">|&gt;</span> <span class="fu">ungroup</span>() <span class="sc">|&gt;</span> </span>
<span id="cb158-2"><a href="#cb158-2" tabindex="-1"></a>  <span class="fu">filter</span>(crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb158-3"><a href="#cb158-3" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">time_since_0 =</span> <span class="dv">0</span>) <span class="sc">|&gt;</span> </span>
<span id="cb158-4"><a href="#cb158-4" tabindex="-1"></a>  <span class="fu">select</span>(time_since_0, <span class="at">ratio_woman =</span> Ratio)</span></code></pre></div>
<pre><code>## ungroup: no grouping variables</code></pre>
<pre><code>## filter: removed 3 rows (75%), one row remaining</code></pre>
<pre><code>## mutate: new variable &#39;time_since_0&#39; (double) with one unique value and 0% NA</code></pre>
<pre><code>## select: renamed one variable (ratio_woman) and dropped 3 variables</code></pre>
<div class="sourceCode" id="cb163"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb163-1"><a href="#cb163-1" tabindex="-1"></a>fmrisk_t0</span></code></pre></div>
<pre><code>## # A tibble: 1 × 2
##   time_since_0 ratio_woman
##          &lt;dbl&gt;       &lt;dbl&gt;
## 1            0      0.0385</code></pre>
<ul>
<li>During a non-crisis time, 3.85% of ministers are women.<br />
</li>
<li>During a crisis time, 5.24% of ministers are women.</li>
</ul>
<p>At t = - 1</p>
<p>Calculate the “risk” at time t - 1 (one month before a crisis
begins).</p>
<div class="sourceCode" id="cb165"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb165-1"><a href="#cb165-1" tabindex="-1"></a>fmrisk_t1 <span class="ot">&lt;-</span> df_spell <span class="sc">|&gt;</span> </span>
<span id="cb165-2"><a href="#cb165-2" tabindex="-1"></a>  <span class="fu">arrange</span>(iso3c, date) <span class="sc">|&gt;</span> </span>
<span id="cb165-3"><a href="#cb165-3" tabindex="-1"></a>  <span class="fu">group_by</span>(iso3c) <span class="sc">|&gt;</span> </span>
<span id="cb165-4"><a href="#cb165-4" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">crisis_next =</span> <span class="fu">lead</span>(crisis_mon)) <span class="sc">|&gt;</span> </span>
<span id="cb165-5"><a href="#cb165-5" tabindex="-1"></a>  <span class="fu">filter</span>(crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_next <span class="sc">==</span> <span class="dv">1</span>)</span></code></pre></div>
<pre><code>## group_by: one grouping variable (iso3c)</code></pre>
<pre><code>## mutate (grouped): new variable &#39;crisis_next&#39; (double) with 3 unique values and &lt;1% NA</code></pre>
<pre><code>## filter (grouped): removed 100,365 rows (&gt;99%), 147 rows remaining</code></pre>
<div class="sourceCode" id="cb169"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb169-1"><a href="#cb169-1" tabindex="-1"></a>fmrisk_t1 <span class="ot">&lt;-</span> fmrisk_t1 <span class="sc">|&gt;</span> <span class="fu">group_by</span>(fm_gender) <span class="sc">|&gt;</span> </span>
<span id="cb169-2"><a href="#cb169-2" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">N =</span> <span class="fu">n</span>()) <span class="sc">|&gt;</span> </span>
<span id="cb169-3"><a href="#cb169-3" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Ratio =</span> <span class="fu">round</span>(N <span class="sc">/</span> <span class="fu">sum</span>(N), <span class="dv">4</span>)) <span class="sc">|&gt;</span> </span>
<span id="cb169-4"><a href="#cb169-4" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb169-5"><a href="#cb169-5" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">time_since_0 =</span> <span class="sc">-</span><span class="dv">1</span>) <span class="sc">|&gt;</span>  </span>
<span id="cb169-6"><a href="#cb169-6" tabindex="-1"></a>  <span class="fu">select</span>(time_since_0, <span class="at">ratio_woman =</span> Ratio)</span></code></pre></div>
<pre><code>## group_by: one grouping variable (fm_gender)</code></pre>
<pre><code>## summarise: now 2 rows and 2 columns, ungrouped</code></pre>
<pre><code>## mutate: new variable &#39;Ratio&#39; (double) with 2 unique values and 0% NA</code></pre>
<pre><code>## filter: removed one row (50%), one row remaining</code></pre>
<pre><code>## mutate: new variable &#39;time_since_0&#39; (double) with one unique value and 0% NA</code></pre>
<pre><code>## select: renamed one variable (ratio_woman) and dropped 2 variables</code></pre>
<p>At t = 0</p>
<p>Calculate the “risk” of woman holding FM-ship during a crisis over
time.</p>
<div class="sourceCode" id="cb176"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb176-1"><a href="#cb176-1" tabindex="-1"></a>fmrisk <span class="ot">&lt;-</span> df_spell <span class="sc">|&gt;</span> </span>
<span id="cb176-2"><a href="#cb176-2" tabindex="-1"></a>  <span class="fu">filter</span>(crisis_mon <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb176-3"><a href="#cb176-3" tabindex="-1"></a>  <span class="fu">group_by</span>(time_since_0, fm_gender) <span class="sc">|&gt;</span> </span>
<span id="cb176-4"><a href="#cb176-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">N =</span> <span class="fu">n</span>()) <span class="sc">|&gt;</span> </span>
<span id="cb176-5"><a href="#cb176-5" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Ratio =</span> <span class="fu">round</span>(N <span class="sc">/</span> <span class="fu">sum</span>(N), <span class="dv">4</span>)) <span class="sc">|&gt;</span> </span>
<span id="cb176-6"><a href="#cb176-6" tabindex="-1"></a>  <span class="fu">filter</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb176-7"><a href="#cb176-7" tabindex="-1"></a>  <span class="fu">select</span>(time_since_0, <span class="at">ratio_woman =</span> Ratio) <span class="sc">|&gt;</span> </span>
<span id="cb176-8"><a href="#cb176-8" tabindex="-1"></a>  <span class="fu">ungroup</span>()</span></code></pre></div>
<pre><code>## filter: removed 95,401 rows (95%), 5,111 rows remaining</code></pre>
<pre><code>## group_by: 2 grouping variables (time_since_0, fm_gender)</code></pre>
<pre><code>## summarise: now 154 rows and 3 columns, one group variable remaining (time_since_0)</code></pre>
<pre><code>## mutate (grouped): new variable &#39;Ratio&#39; (double) with 71 unique values and 0% NA</code></pre>
<pre><code>## filter (grouped): removed 96 rows (62%), 58 rows remaining</code></pre>
<pre><code>## select: renamed one variable (ratio_woman) and dropped 2 variables</code></pre>
<pre><code>## ungroup: no grouping variables</code></pre>
<p>Combine t=-1, t=0, and during crisis.</p>
<div class="sourceCode" id="cb184"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb184-1"><a href="#cb184-1" tabindex="-1"></a>fmrisk <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(fmrisk_t1, fmrisk)</span>
<span id="cb184-2"><a href="#cb184-2" tabindex="-1"></a>fmrisk</span></code></pre></div>
<pre><code>## # A tibble: 59 × 2
##    time_since_0 ratio_woman
##           &lt;dbl&gt;       &lt;dbl&gt;
##  1           -1      0.0272
##  2            1      0.0201
##  3            2      0.0268
##  4            3      0.0408
##  5            4      0.0408
##  6            5      0.0411
##  7            6      0.0414
##  8            7      0.0486
##  9            8      0.0486
## 10            9      0.0563
## # ℹ 49 more rows</code></pre>
<p>“Risk” of a woman holding a finance minister office during a
crisis.</p>
<div class="sourceCode" id="cb186"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb186-1"><a href="#cb186-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> fmrisk) <span class="sc">+</span> </span>
<span id="cb186-2"><a href="#cb186-2" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> fmrisk_t0 <span class="sc">$</span> ratio_woman <span class="sc">*</span> <span class="dv">100</span>, <span class="at">linetype =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb186-3"><a href="#cb186-3" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x =</span> time_since_0, <span class="at">y =</span> ratio_woman <span class="sc">*</span> <span class="dv">100</span>)) <span class="sc">+</span> </span>
<span id="cb186-4"><a href="#cb186-4" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">8</span>), </span>
<span id="cb186-5"><a href="#cb186-5" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">2</span>, <span class="dv">4</span>, <span class="dv">6</span>, <span class="dv">8</span>), </span>
<span id="cb186-6"><a href="#cb186-6" tabindex="-1"></a>                     <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;0%&quot;</span>, <span class="st">&quot;2%&quot;</span>, <span class="st">&quot;4%&quot;</span>, <span class="st">&quot;6%&quot;</span>, <span class="st">&quot;8%&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb186-7"><a href="#cb186-7" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">1</span>, <span class="dv">60</span>), </span>
<span id="cb186-8"><a href="#cb186-8" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">12</span>, <span class="dv">24</span>, <span class="dv">36</span>, <span class="dv">48</span>, <span class="dv">60</span>),</span>
<span id="cb186-9"><a href="#cb186-9" tabindex="-1"></a>                     <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;0&quot;</span>, <span class="st">&quot;1&quot;</span>, <span class="st">&quot;2&quot;</span>, <span class="st">&quot;3&quot;</span>, <span class="st">&quot;4&quot;</span>, <span class="st">&quot;5&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb186-10"><a href="#cb186-10" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Years since the beginning of a banking crisis&quot;</span>, </span>
<span id="cb186-11"><a href="#cb186-11" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Percentage of woman finance ministers&quot;</span>,</span>
<span id="cb186-12"><a href="#cb186-12" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span> </span>
<span id="cb186-13"><a href="#cb186-13" tabindex="-1"></a>  <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb187"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb187-1"><a href="#cb187-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureA1_GenderRatio.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">6</span>)</span></code></pre></div>
</div>
</div>
<div id="womens-initial-access" class="section level1">
<h1>2. Women’s Initial Access</h1>
<div id="main-analysis" class="section level2">
<h2>Main Analysis</h2>
<div id="table-1" class="section level3">
<h3>Table 1</h3>
<p>Estimate five models reported in Table 1.</p>
<div class="sourceCode" id="cb188"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb188-1"><a href="#cb188-1" tabindex="-1"></a><span class="do">## Base model formula</span></span>
<span id="cb188-2"><a href="#cb188-2" tabindex="-1"></a>base_fml <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(fwfm72_t0, fwfm72_t, fwfm72_d) <span class="sc">~</span>  .)</span>
<span id="cb188-3"><a href="#cb188-3" tabindex="-1"></a></span>
<span id="cb188-4"><a href="#cb188-4" tabindex="-1"></a><span class="do">## Estimate five models</span></span>
<span id="cb188-5"><a href="#cb188-5" tabindex="-1"></a>wia_m1 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(base_fml, . <span class="sc">~</span> <span class="sc">+</span> crisis_mon), </span>
<span id="cb188-6"><a href="#cb188-6" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> iso3c, <span class="at">ties=</span><span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>))</span>
<span id="cb188-7"><a href="#cb188-7" tabindex="-1"></a><span class="do">## + woman </span></span>
<span id="cb188-8"><a href="#cb188-8" tabindex="-1"></a>wia_m2 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb188-9"><a href="#cb188-9" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb188-10"><a href="#cb188-10" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender))</span>
<span id="cb188-11"><a href="#cb188-11" tabindex="-1"></a><span class="do">## + institution</span></span>
<span id="cb188-12"><a href="#cb188-12" tabindex="-1"></a>wia_m3 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb188-13"><a href="#cb188-13" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb188-14"><a href="#cb188-14" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut))</span>
<span id="cb188-15"><a href="#cb188-15" tabindex="-1"></a><span class="do">## + economic</span></span>
<span id="cb188-16"><a href="#cb188-16" tabindex="-1"></a>wia_m4 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb188-17"><a href="#cb188-17" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb188-18"><a href="#cb188-18" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb188-19"><a href="#cb188-19" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag))</span>
<span id="cb188-20"><a href="#cb188-20" tabindex="-1"></a><span class="do">## full model</span></span>
<span id="cb188-21"><a href="#cb188-21" tabindex="-1"></a>wia_m5 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb188-22"><a href="#cb188-22" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb188-23"><a href="#cb188-23" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb188-24"><a href="#cb188-24" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb188-25"><a href="#cb188-25" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb188-26"><a href="#cb188-26" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag))</span>
<span id="cb188-27"><a href="#cb188-27" tabindex="-1"></a></span>
<span id="cb188-28"><a href="#cb188-28" tabindex="-1"></a></span>
<span id="cb188-29"><a href="#cb188-29" tabindex="-1"></a><span class="do">## Put them together</span></span>
<span id="cb188-30"><a href="#cb188-30" tabindex="-1"></a>model_list_tbl1 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m1, wia_m2, wia_m3, wia_m4, wia_m5)</span>
<span id="cb188-31"><a href="#cb188-31" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb188-32"><a href="#cb188-32" tabindex="-1"></a>model_list_tbl1 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tbl1, add_AIC)</span></code></pre></div>
<p>Show hazard rate coefficients.</p>
<div class="sourceCode" id="cb189"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb189-1"><a href="#cb189-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tbl1,</span>
<span id="cb189-2"><a href="#cb189-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tbl1, get_clse_hr),</span>
<span id="cb189-3"><a href="#cb189-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb189-4"><a href="#cb189-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb189-5"><a href="#cb189-5" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb189-6"><a href="#cb189-6" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, </span>
<span id="cb189-7"><a href="#cb189-7" tabindex="-1"></a>                               <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb189-8"><a href="#cb189-8" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, </span>
<span id="cb189-9"><a href="#cb189-9" tabindex="-1"></a>                               <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb189-10"><a href="#cb189-10" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, </span>
<span id="cb189-11"><a href="#cb189-11" tabindex="-1"></a>                               <span class="st">&quot;Inflation&quot;</span>, </span>
<span id="cb189-12"><a href="#cb189-12" tabindex="-1"></a>                               <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb189-13"><a href="#cb189-13" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, </span>
<span id="cb189-14"><a href="#cb189-14" tabindex="-1"></a>                               <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb189-15"><a href="#cb189-15" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb189-16"><a href="#cb189-16" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb189-17"><a href="#cb189-17" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table 1: Banking Crisis and Women&#39;s Initial Access to the Finance Ministry, 1972-2017&quot;</span>,</span>
<span id="cb189-18"><a href="#cb189-18" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb189-19"><a href="#cb189-19" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/table1.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table 1: Banking Crisis and Women’s Initial Access to the
Finance Ministry, 1972-2017</strong>
</caption>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="5">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
<td>
(5)
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.040<sup>***</sup>
</td>
<td>
1.061<sup>***</sup>
</td>
<td>
0.860<sup>**</sup>
</td>
<td>
1.274<sup>***</sup>
</td>
<td>
1.130<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.359)
</td>
<td>
(0.386)
</td>
<td>
(0.374)
</td>
<td>
(0.391)
</td>
<td>
(0.448)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
</td>
<td>
0.055<sup>***</sup>
</td>
<td>
</td>
<td>
</td>
<td>
0.051<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.010)
</td>
<td>
</td>
<td>
</td>
<td>
(0.013)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
</td>
<td>
0.778<sup>*</sup>
</td>
<td>
</td>
<td>
</td>
<td>
0.625
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.465)
</td>
<td>
</td>
<td>
</td>
<td>
(0.554)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
</td>
<td>
</td>
<td>
0.609<sup>**</sup>
</td>
<td>
</td>
<td>
0.288
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.265)
</td>
<td>
</td>
<td>
(0.301)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
</td>
<td>
</td>
<td>
0.543<sup>***</sup>
</td>
<td>
</td>
<td>
0.888<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.129)
</td>
<td>
</td>
<td>
(0.245)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
</td>
<td>
</td>
<td>
-0.119
</td>
<td>
</td>
<td>
-0.054
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.267)
</td>
<td>
</td>
<td>
(0.294)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.020
</td>
<td>
0.044<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.023)
</td>
<td>
(0.022)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.005
</td>
<td>
-0.020
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.034)
</td>
<td>
(0.104)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.036
</td>
<td>
-0.374<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.117)
</td>
<td>
(0.183)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.0002
</td>
<td>
0.001
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.002)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.022
</td>
<td>
-0.098
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.115)
</td>
<td>
(0.147)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
85,092
</td>
<td>
68,792
</td>
<td>
67,641
</td>
<td>
72,380
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-369.201
</td>
<td>
-292.309
</td>
<td>
-287.882
</td>
<td>
-303.186
</td>
<td>
-222.850
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
740.402
</td>
<td>
590.617
</td>
<td>
583.765
</td>
<td>
618.373
</td>
<td>
467.699
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="5" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>We calculate N for each model. We first prepare smaller data sets for
different models.</p>
<div class="sourceCode" id="cb190"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb190-1"><a href="#cb190-1" tabindex="-1"></a>df_m1 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, iso3c) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span>
<span id="cb190-2"><a href="#cb190-2" tabindex="-1"></a>df_m2 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, iso3c, </span>
<span id="cb190-3"><a href="#cb190-3" tabindex="-1"></a>                      womenrep_lag, leadgender) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span>
<span id="cb190-4"><a href="#cb190-4" tabindex="-1"></a>df_m3 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, iso3c, </span>
<span id="cb190-5"><a href="#cb190-5" tabindex="-1"></a>                      presidential, xm_qudsest, unified_aut) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span>
<span id="cb190-6"><a href="#cb190-6" tabindex="-1"></a>df_m4 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, iso3c, </span>
<span id="cb190-7"><a href="#cb190-7" tabindex="-1"></a>                      growth_lag, inflate_lag, loggdppc_lag, </span>
<span id="cb190-8"><a href="#cb190-8" tabindex="-1"></a>                      tradepergdp_i_lag, kaopen_i_lag) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span>
<span id="cb190-9"><a href="#cb190-9" tabindex="-1"></a>df_m5 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, iso3c, </span>
<span id="cb190-10"><a href="#cb190-10" tabindex="-1"></a>                      womenrep_lag, leadgender,</span>
<span id="cb190-11"><a href="#cb190-11" tabindex="-1"></a>                      presidential, xm_qudsest, unified_aut,</span>
<span id="cb190-12"><a href="#cb190-12" tabindex="-1"></a>                      growth_lag, inflate_lag, loggdppc_lag, </span>
<span id="cb190-13"><a href="#cb190-13" tabindex="-1"></a>                      tradepergdp_i_lag, kaopen_i_lag) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span></code></pre></div>
<div class="sourceCode" id="cb191"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb191-1"><a href="#cb191-1" tabindex="-1"></a>report_n <span class="ot">&lt;-</span> <span class="cf">function</span>(df){</span>
<span id="cb191-2"><a href="#cb191-2" tabindex="-1"></a>  <span class="fu">data.frame</span>(<span class="at">n =</span> <span class="fu">dim</span>(df)[<span class="dv">1</span>], </span>
<span id="cb191-3"><a href="#cb191-3" tabindex="-1"></a>             <span class="at">n_cntry =</span> <span class="fu">length</span>(<span class="fu">unique</span>(df <span class="sc">$</span> iso3c)),</span>
<span id="cb191-4"><a href="#cb191-4" tabindex="-1"></a>             <span class="at">n_fail =</span> <span class="fu">sum</span>(df <span class="sc">$</span> fail <span class="sc">==</span> <span class="dv">1</span>))</span>
<span id="cb191-5"><a href="#cb191-5" tabindex="-1"></a>}</span>
<span id="cb191-6"><a href="#cb191-6" tabindex="-1"></a>res_n <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">list</span>(df_m1, df_m2, df_m3, df_m4, df_m5), report_n)</span>
<span id="cb191-7"><a href="#cb191-7" tabindex="-1"></a>res_n <span class="ot">&lt;-</span> <span class="fu">unlist</span>(res_n) <span class="sc">|&gt;</span> <span class="fu">matrix</span>(<span class="at">byrow=</span>F, <span class="at">ncol=</span><span class="dv">5</span>) <span class="sc">|&gt;</span> <span class="fu">data.frame</span>()</span>
<span id="cb191-8"><a href="#cb191-8" tabindex="-1"></a><span class="fu">rownames</span>(res_n) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;N. Obs&quot;</span>, <span class="st">&quot;N. Country&quot;</span>, <span class="st">&quot;N. Failures&quot;</span>)</span>
<span id="cb191-9"><a href="#cb191-9" tabindex="-1"></a><span class="fu">colnames</span>(res_n) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Model 1&quot;</span>, <span class="st">&quot;Model 2&quot;</span>, <span class="st">&quot;Model 3&quot;</span>, <span class="st">&quot;Model 4&quot;</span>, <span class="st">&quot;Model 5&quot;</span>)</span>
<span id="cb191-10"><a href="#cb191-10" tabindex="-1"></a><span class="fu">kable</span>(res_n)</span></code></pre></div>
<table>
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Model 1
</th>
<th style="text-align:right;">
Model 2
</th>
<th style="text-align:right;">
Model 3
</th>
<th style="text-align:right;">
Model 4
</th>
<th style="text-align:right;">
Model 5
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
N. Obs
</td>
<td style="text-align:right;">
85092
</td>
<td style="text-align:right;">
68792
</td>
<td style="text-align:right;">
67641
</td>
<td style="text-align:right;">
72380
</td>
<td style="text-align:right;">
53805
</td>
</tr>
<tr>
<td style="text-align:left;">
N. Country
</td>
<td style="text-align:right;">
199
</td>
<td style="text-align:right;">
182
</td>
<td style="text-align:right;">
164
</td>
<td style="text-align:right;">
169
</td>
<td style="text-align:right;">
150
</td>
</tr>
<tr>
<td style="text-align:left;">
N. Failures
</td>
<td style="text-align:right;">
75
</td>
<td style="text-align:right;">
65
</td>
<td style="text-align:right;">
63
</td>
<td style="text-align:right;">
64
</td>
<td style="text-align:right;">
55
</td>
</tr>
</tbody>
</table>
</div>
<div id="figure-1" class="section level3">
<h3>Figure 1</h3>
<p>We will plot exponentiated coefficients (Hazard ratio).</p>
<div class="sourceCode" id="cb192"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb192-1"><a href="#cb192-1" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">pe =</span> <span class="cn">NA</span>, <span class="at">low =</span> <span class="cn">NA</span>, <span class="at">up =</span> <span class="cn">NA</span>)</span>
<span id="cb192-2"><a href="#cb192-2" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(model_list_tbl1)){</span>
<span id="cb192-3"><a href="#cb192-3" tabindex="-1"></a>  df_hr[i, ] <span class="ot">&lt;-</span> <span class="fu">extract_hr</span>(model_list_tbl1[[i]])</span>
<span id="cb192-4"><a href="#cb192-4" tabindex="-1"></a>}</span>
<span id="cb192-5"><a href="#cb192-5" tabindex="-1"></a></span>
<span id="cb192-6"><a href="#cb192-6" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> df_hr <span class="sc">|&gt;</span> </span>
<span id="cb192-7"><a href="#cb192-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">model =</span> <span class="fu">c</span>(<span class="st">&quot;Model 1: Bivariate</span><span class="sc">\n</span><span class="st">199 countries</span><span class="sc">\n</span><span class="st">75 women&quot;</span>, </span>
<span id="cb192-8"><a href="#cb192-8" tabindex="-1"></a>                   <span class="st">&quot;Model 2: + Woman</span><span class="sc">\n</span><span class="st">182 countries</span><span class="sc">\n</span><span class="st">65 women&quot;</span>, </span>
<span id="cb192-9"><a href="#cb192-9" tabindex="-1"></a>                   <span class="st">&quot;Model 3: + Institution</span><span class="sc">\n</span><span class="st">164 countries</span><span class="sc">\n</span><span class="st">63 women&quot;</span>, </span>
<span id="cb192-10"><a href="#cb192-10" tabindex="-1"></a>                   <span class="st">&quot;Model 4: + Economic</span><span class="sc">\n</span><span class="st">169 countries</span><span class="sc">\n</span><span class="st">64 women&quot;</span>, </span>
<span id="cb192-11"><a href="#cb192-11" tabindex="-1"></a>                   <span class="st">&quot;Model 5: Full</span><span class="sc">\n</span><span class="st">150 countries</span><span class="sc">\n</span><span class="st">55 women&quot;</span>), </span>
<span id="cb192-12"><a href="#cb192-12" tabindex="-1"></a>         <span class="at">mnum =</span> <span class="fu">seq</span>(<span class="dv">5</span>, <span class="dv">1</span>))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;model&#39; (character) with 5 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;mnum&#39; (integer) with 5 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb195"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb195-1"><a href="#cb195-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> df_hr, </span>
<span id="cb195-2"><a href="#cb195-2" tabindex="-1"></a>       <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> pe, <span class="at">y =</span> <span class="fu">reorder</span>(model, mnum))) <span class="sc">+</span> </span>
<span id="cb195-3"><a href="#cb195-3" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="dv">2</span>, <span class="at">alpha =</span> <span class="fl">0.4</span>) <span class="sc">+</span> </span>
<span id="cb195-4"><a href="#cb195-4" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> low, <span class="at">xend =</span> up, <span class="at">yend =</span> <span class="fu">reorder</span>(model, mnum))) <span class="sc">+</span> </span>
<span id="cb195-5"><a href="#cb195-5" tabindex="-1"></a>  <span class="fu">geom_label</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">round</span>(pe, <span class="dv">2</span>))) <span class="sc">+</span> </span>
<span id="cb195-6"><a href="#cb195-6" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">3</span>,<span class="dv">5</span>,<span class="dv">7</span>)) <span class="sc">+</span> </span>
<span id="cb195-7"><a href="#cb195-7" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Hazard ratio for banking crisis&quot;</span>, <span class="at">y =</span> <span class="cn">NULL</span>,</span>
<span id="cb195-8"><a href="#cb195-8" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span> </span>
<span id="cb195-9"><a href="#cb195-9" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb195-10"><a href="#cb195-10" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))</span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb196"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb196-1"><a href="#cb196-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/Figure2_InitialAccess.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>)</span></code></pre></div>
</div>
</div>
<div id="testing-non-proportionality" class="section level2">
<h2>Testing non-proportionality</h2>
<p>We follow Park and Hendry’s (2015) approach to test the
non-proportionality using different transformations of time.</p>
<div id="table-b10" class="section level3">
<h3>Table B10</h3>
<p>Model 1</p>
<div class="sourceCode" id="cb197"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb197-1"><a href="#cb197-1" tabindex="-1"></a><span class="fu">zphTable</span>(wia_m1, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##            km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon    0.065     1 0.798      0.446       1  0.504
## GLOBAL        0.065     1 0.798      0.446       1  0.504</code></pre>
<p>Model 2</p>
<div class="sourceCode" id="cb199"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb199-1"><a href="#cb199-1" tabindex="-1"></a><span class="fu">zphTable</span>(wia_m2, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##              km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon      1.697     1 0.193      2.994       1  0.084
## womenrep_lag    1.659     1 0.198      1.568       1  0.210
## leadgender      0.055     1 0.815      0.051       1  0.821
## GLOBAL          3.416     3 0.332      4.691       3  0.196</code></pre>
<p>Model 3</p>
<div class="sourceCode" id="cb201"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb201-1"><a href="#cb201-1" tabindex="-1"></a><span class="fu">zphTable</span>(wia_m3, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##              km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon      0.164     1 0.685      0.491       1  0.483
## presidential    0.641     1 0.423      0.891       1  0.345
## xm_qudsest      2.412     1 0.120      2.532       1  0.112
## unified_aut     1.485     1 0.223      0.612       1  0.434
## GLOBAL          5.511     4 0.239      6.365       4  0.173</code></pre>
<p>Model 4</p>
<div class="sourceCode" id="cb203"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb203-1"><a href="#cb203-1" tabindex="-1"></a><span class="fu">zphTable</span>(wia_m4, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##                   km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon           0.299     1 0.585      1.379       1  0.240
## growth_lag           0.988     1 0.320      1.952       1  0.162
## inflate_lag          0.756     1 0.385      1.286       1  0.257
## loggdppc_lag         2.065     1 0.151      1.728       1  0.189
## tradepergdp_i_lag    4.493     1 0.034      5.516       1  0.019
## kaopen_i_lag         2.829     1 0.093      3.117       1  0.077
## GLOBAL               7.633     6 0.266     10.440       6  0.107</code></pre>
<p>Model 5</p>
<div class="sourceCode" id="cb205"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb205-1"><a href="#cb205-1" tabindex="-1"></a><span class="fu">zphTable</span>(wia_m5, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##                   km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon           1.584     1 0.208      3.339       1  0.068
## womenrep_lag         2.238     1 0.135      3.004       1  0.083
## leadgender           0.165     1 0.684      0.176       1  0.675
## presidential         0.203     1 0.652      0.313       1  0.576
## xm_qudsest           0.602     1 0.438      0.653       1  0.419
## unified_aut          0.658     1 0.417      0.106       1  0.745
## growth_lag           0.602     1 0.438      0.226       1  0.635
## inflate_lag          5.066     1 0.024      2.462       1  0.117
## loggdppc_lag         0.002     1 0.968      0.014       1  0.906
## tradepergdp_i_lag    0.587     1 0.444      0.790       1  0.374
## kaopen_i_lag         0.025     1 0.875      0.175       1  0.676
## GLOBAL              14.237    11 0.220     13.598      11  0.256</code></pre>
<p>Summary of Results</p>
<div class="sourceCode" id="cb207"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb207-1"><a href="#cb207-1" tabindex="-1"></a>zph_m1 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(wia_m1, <span class="at">digits =</span> <span class="dv">2</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb207-2"><a href="#cb207-2" tabindex="-1"></a>zph_m2 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(wia_m2, <span class="at">digits =</span> <span class="dv">2</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb207-3"><a href="#cb207-3" tabindex="-1"></a>zph_m3 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(wia_m3, <span class="at">digits =</span> <span class="dv">2</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb207-4"><a href="#cb207-4" tabindex="-1"></a>zph_m4 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(wia_m4, <span class="at">digits =</span> <span class="dv">2</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb207-5"><a href="#cb207-5" tabindex="-1"></a>zph_m5 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(wia_m5, <span class="at">digits =</span> <span class="dv">2</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb207-6"><a href="#cb207-6" tabindex="-1"></a></span>
<span id="cb207-7"><a href="#cb207-7" tabindex="-1"></a>zphdf_m1 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m1), <span class="at">km.p1 =</span> zph_m1[,<span class="dv">1</span>], <span class="at">rank.p1 =</span> zph_m1[,<span class="dv">2</span>])</span>
<span id="cb207-8"><a href="#cb207-8" tabindex="-1"></a>zphdf_m2 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m2), <span class="at">km.p2 =</span> zph_m2[,<span class="dv">1</span>], <span class="at">rank.p2 =</span> zph_m2[,<span class="dv">2</span>])</span>
<span id="cb207-9"><a href="#cb207-9" tabindex="-1"></a>zphdf_m3 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m3), <span class="at">km.p3 =</span> zph_m3[,<span class="dv">1</span>], <span class="at">rank.p3 =</span> zph_m3[,<span class="dv">2</span>])</span>
<span id="cb207-10"><a href="#cb207-10" tabindex="-1"></a>zphdf_m4 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m4), <span class="at">km.p4 =</span> zph_m4[,<span class="dv">1</span>], <span class="at">rank.p4 =</span> zph_m4[,<span class="dv">2</span>])</span>
<span id="cb207-11"><a href="#cb207-11" tabindex="-1"></a>zphdf_m5 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m5), <span class="at">km.p5 =</span> zph_m5[,<span class="dv">1</span>], <span class="at">rank.p5 =</span> zph_m5[,<span class="dv">2</span>])</span>
<span id="cb207-12"><a href="#cb207-12" tabindex="-1"></a></span>
<span id="cb207-13"><a href="#cb207-13" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_m1, zphdf_m2)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p2, rank.p2)
## &gt; rows only in x 0
## &gt; rows only in y 2
## &gt; matched rows 2
## &gt; ===
## &gt; rows total 4</code></pre>
<div class="sourceCode" id="cb209"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb209-1"><a href="#cb209-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m3)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p3, rank.p3)
## &gt; rows only in x 2
## &gt; rows only in y 3
## &gt; matched rows 2
## &gt; ===
## &gt; rows total 7</code></pre>
<div class="sourceCode" id="cb211"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb211-1"><a href="#cb211-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m4)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p4, rank.p4)
## &gt; rows only in x 5
## &gt; rows only in y 5
## &gt; matched rows 2
## &gt; ====
## &gt; rows total 12</code></pre>
<div class="sourceCode" id="cb213"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb213-1"><a href="#cb213-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m5)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p5, rank.p5)
## &gt; rows only in x 0
## &gt; rows only in y 0
## &gt; matched rows 12
## &gt; ====
## &gt; rows total 12</code></pre>
<div class="sourceCode" id="cb215"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb215-1"><a href="#cb215-1" tabindex="-1"></a>xid <span class="ot">&lt;-</span> <span class="fu">which</span>(zphdf_sum <span class="sc">$</span> x <span class="sc">==</span> <span class="st">&quot;GLOBAL&quot;</span>)</span>
<span id="cb215-2"><a href="#cb215-2" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">rbind</span>(zphdf_sum[<span class="sc">-</span>xid, ], zphdf_sum[xid, ])</span>
<span id="cb215-3"><a href="#cb215-3" tabindex="-1"></a>zphdf_sum <span class="sc">$</span> x <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb215-4"><a href="#cb215-4" tabindex="-1"></a>                   <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb215-5"><a href="#cb215-5" tabindex="-1"></a>                   <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb215-6"><a href="#cb215-6" tabindex="-1"></a>                   <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb215-7"><a href="#cb215-7" tabindex="-1"></a>                   <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>, <span class="st">&quot;Global&quot;</span>)</span></code></pre></div>
<p>Summary table</p>
<div class="sourceCode" id="cb216"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb216-1"><a href="#cb216-1" tabindex="-1"></a><span class="fu">kable</span>(zphdf_sum, <span class="at">row.names =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb216-2"><a href="#cb216-2" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb216-3"><a href="#cb216-3" tabindex="-1"></a>  <span class="fu">add_header_above</span>(<span class="fu">c</span>(<span class="st">&quot; &quot;</span> <span class="ot">=</span> <span class="dv">1</span>, <span class="st">&quot;Model 1&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 2&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 3&quot;</span> <span class="ot">=</span> <span class="dv">2</span>,</span>
<span id="cb216-4"><a href="#cb216-4" tabindex="-1"></a>                     <span class="st">&quot;Model 4&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 5&quot;</span> <span class="ot">=</span> <span class="dv">2</span>))</span></code></pre></div>
<table class=" lightable-classic" style="font-family: &quot;Arial Narrow&quot;, &quot;Source Sans Pro&quot;, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="empty-cells: hide;" colspan="1">
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 1
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 2
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 3
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 4
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 5
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
x
</th>
<th style="text-align:right;">
km.p1
</th>
<th style="text-align:right;">
rank.p1
</th>
<th style="text-align:right;">
km.p2
</th>
<th style="text-align:right;">
rank.p2
</th>
<th style="text-align:right;">
km.p3
</th>
<th style="text-align:right;">
rank.p3
</th>
<th style="text-align:right;">
km.p4
</th>
<th style="text-align:right;">
rank.p4
</th>
<th style="text-align:right;">
km.p5
</th>
<th style="text-align:right;">
rank.p5
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Banking Crisis
</td>
<td style="text-align:right;">
0.8
</td>
<td style="text-align:right;">
0.5
</td>
<td style="text-align:right;">
0.19
</td>
<td style="text-align:right;">
0.08
</td>
<td style="text-align:right;">
0.69
</td>
<td style="text-align:right;">
0.48
</td>
<td style="text-align:right;">
0.58
</td>
<td style="text-align:right;">
0.24
</td>
<td style="text-align:right;">
0.21
</td>
<td style="text-align:right;">
0.07
</td>
</tr>
<tr>
<td style="text-align:left;">
Women in Parliament
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.20
</td>
<td style="text-align:right;">
0.21
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.13
</td>
<td style="text-align:right;">
0.08
</td>
</tr>
<tr>
<td style="text-align:left;">
Woman Head of Government
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.82
</td>
<td style="text-align:right;">
0.82
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.68
</td>
<td style="text-align:right;">
0.68
</td>
</tr>
<tr>
<td style="text-align:left;">
Presidential
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.42
</td>
<td style="text-align:right;">
0.35
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.65
</td>
<td style="text-align:right;">
0.58
</td>
</tr>
<tr>
<td style="text-align:left;">
Democracy Score
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.12
</td>
<td style="text-align:right;">
0.11
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.44
</td>
<td style="text-align:right;">
0.42
</td>
</tr>
<tr>
<td style="text-align:left;">
Unified Government
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.22
</td>
<td style="text-align:right;">
0.43
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.42
</td>
<td style="text-align:right;">
0.74
</td>
</tr>
<tr>
<td style="text-align:left;">
Economic Growth
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.32
</td>
<td style="text-align:right;">
0.16
</td>
<td style="text-align:right;">
0.44
</td>
<td style="text-align:right;">
0.63
</td>
</tr>
<tr>
<td style="text-align:left;">
Inflation
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.38
</td>
<td style="text-align:right;">
0.26
</td>
<td style="text-align:right;">
0.02
</td>
<td style="text-align:right;">
0.12
</td>
</tr>
<tr>
<td style="text-align:left;">
GDP per Capita
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.15
</td>
<td style="text-align:right;">
0.19
</td>
<td style="text-align:right;">
0.97
</td>
<td style="text-align:right;">
0.91
</td>
</tr>
<tr>
<td style="text-align:left;">
Trade Openness
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.03
</td>
<td style="text-align:right;">
0.02
</td>
<td style="text-align:right;">
0.44
</td>
<td style="text-align:right;">
0.37
</td>
</tr>
<tr>
<td style="text-align:left;">
Capital Openness
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.09
</td>
<td style="text-align:right;">
0.08
</td>
<td style="text-align:right;">
0.88
</td>
<td style="text-align:right;">
0.68
</td>
</tr>
<tr>
<td style="text-align:left;">
Global
</td>
<td style="text-align:right;">
0.8
</td>
<td style="text-align:right;">
0.5
</td>
<td style="text-align:right;">
0.33
</td>
<td style="text-align:right;">
0.20
</td>
<td style="text-align:right;">
0.24
</td>
<td style="text-align:right;">
0.17
</td>
<td style="text-align:right;">
0.27
</td>
<td style="text-align:right;">
0.11
</td>
<td style="text-align:right;">
0.22
</td>
<td style="text-align:right;">
0.26
</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb217"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb217-1"><a href="#cb217-1" tabindex="-1"></a><span class="fu">kable</span>(zphdf_sum, <span class="at">row.names =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb217-2"><a href="#cb217-2" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb217-3"><a href="#cb217-3" tabindex="-1"></a>  <span class="fu">add_header_above</span>(<span class="fu">c</span>(<span class="st">&quot; &quot;</span> <span class="ot">=</span> <span class="dv">1</span>, <span class="st">&quot;Model 1&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 2&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 3&quot;</span> <span class="ot">=</span> <span class="dv">2</span>,</span>
<span id="cb217-4"><a href="#cb217-4" tabindex="-1"></a>                     <span class="st">&quot;Model 4&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 5&quot;</span> <span class="ot">=</span> <span class="dv">2</span>)) <span class="sc">|&gt;</span> </span>
<span id="cb217-5"><a href="#cb217-5" tabindex="-1"></a>  <span class="fu">save_kable</span>(<span class="at">file =</span> <span class="st">&quot;Tables/tableB10.html&quot;</span>, <span class="at">self_contained =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
</div>
<div id="table-b11" class="section level3">
<h3>Table B11</h3>
<p>Global test suggest that none of the model exhibits violation of the
PH assumption.</p>
<p>That being said, note<br />
- for Trade Openness, KM and rank test suggests a possible violation in
Model 4.<br />
- for Inflation, KM test suggests a possible violation in Model 5.</p>
<p>We will thus relax the PH assumption with respect to these variables
for models 4 and 5.</p>
<p>For Model 4: Tests with Kaplan-Meier transformation and rank
transformation both suggest Trade Openness may exhibit
non-proportionality. We estimate models that include an interaction term
between Trade Openness and some function of time.</p>
<div class="sourceCode" id="cb218"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb218-1"><a href="#cb218-1" tabindex="-1"></a><span class="do">## f(t) = t</span></span>
<span id="cb218-2"><a href="#cb218-2" tabindex="-1"></a>wia_m4nph1 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb218-3"><a href="#cb218-3" tabindex="-1"></a>                     <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb218-4"><a href="#cb218-4" tabindex="-1"></a>                              growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb218-5"><a href="#cb218-5" tabindex="-1"></a>                              tradepergdp_i_lag <span class="sc">+</span> <span class="fu">tt</span>(tradepergdp_i_lag) <span class="sc">+</span> </span>
<span id="cb218-6"><a href="#cb218-6" tabindex="-1"></a>                              kaopen_i_lag),</span>
<span id="cb218-7"><a href="#cb218-7" tabindex="-1"></a>                     <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb218-8"><a href="#cb218-8" tabindex="-1"></a><span class="do">## f(t) = log(t)</span></span>
<span id="cb218-9"><a href="#cb218-9" tabindex="-1"></a>wia_m4nph2 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb218-10"><a href="#cb218-10" tabindex="-1"></a>                     <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb218-11"><a href="#cb218-11" tabindex="-1"></a>                              growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb218-12"><a href="#cb218-12" tabindex="-1"></a>                              tradepergdp_i_lag <span class="sc">+</span> <span class="fu">tt</span>(tradepergdp_i_lag) <span class="sc">+</span> </span>
<span id="cb218-13"><a href="#cb218-13" tabindex="-1"></a>                              kaopen_i_lag),</span>
<span id="cb218-14"><a href="#cb218-14" tabindex="-1"></a>                     <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb218-15"><a href="#cb218-15" tabindex="-1"></a><span class="do">## f(t) = sqrt(t)</span></span>
<span id="cb218-16"><a href="#cb218-16" tabindex="-1"></a>wia_m4nph3 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb218-17"><a href="#cb218-17" tabindex="-1"></a>                     <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb218-18"><a href="#cb218-18" tabindex="-1"></a>                              growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb218-19"><a href="#cb218-19" tabindex="-1"></a>                              tradepergdp_i_lag <span class="sc">+</span> <span class="fu">tt</span>(tradepergdp_i_lag) <span class="sc">+</span> </span>
<span id="cb218-20"><a href="#cb218-20" tabindex="-1"></a>                              kaopen_i_lag),</span>
<span id="cb218-21"><a href="#cb218-21" tabindex="-1"></a>                     <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb218-22"><a href="#cb218-22" tabindex="-1"></a>model_list_m4_nph <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m4, wia_m4nph1, wia_m4nph2, wia_m4nph3)</span></code></pre></div>
<p>Compare model fit by AIC.</p>
<div class="sourceCode" id="cb219"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb219-1"><a href="#cb219-1" tabindex="-1"></a><span class="fu">lapply</span>(model_list_m4_nph, extractAIC)</span></code></pre></div>
<pre><code>## [[1]]
## [1]   6.0000 618.3726
## 
## [[2]]
## [1]   7.0000 615.5958
## 
## [[3]]
## [1]   7.0000 617.0683
## 
## [[4]]
## [1]   7.0000 616.0546</code></pre>
<p>Model with identity (linear time) fits the data better.</p>
<p>For Model 5: KM test suggests inflation may exhibit
non-proportionality. We estimate models that include an interaction term
between inflation and some function of time.</p>
<div class="sourceCode" id="cb221"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb221-1"><a href="#cb221-1" tabindex="-1"></a><span class="do">## f(t) = t</span></span>
<span id="cb221-2"><a href="#cb221-2" tabindex="-1"></a>wia_m5nph1 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb221-3"><a href="#cb221-3" tabindex="-1"></a>                     <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb221-4"><a href="#cb221-4" tabindex="-1"></a>                              womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb221-5"><a href="#cb221-5" tabindex="-1"></a>                              presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb221-6"><a href="#cb221-6" tabindex="-1"></a>                              growth_lag <span class="sc">+</span> </span>
<span id="cb221-7"><a href="#cb221-7" tabindex="-1"></a>                              inflate_lag <span class="sc">+</span> <span class="fu">tt</span>(inflate_lag) <span class="sc">+</span>  </span>
<span id="cb221-8"><a href="#cb221-8" tabindex="-1"></a>                              loggdppc_lag <span class="sc">+</span> tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag), </span>
<span id="cb221-9"><a href="#cb221-9" tabindex="-1"></a>                     <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb221-10"><a href="#cb221-10" tabindex="-1"></a><span class="do">## f(t) = log(t)</span></span>
<span id="cb221-11"><a href="#cb221-11" tabindex="-1"></a>wia_m5nph2 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb221-12"><a href="#cb221-12" tabindex="-1"></a>                     <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb221-13"><a href="#cb221-13" tabindex="-1"></a>                              womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb221-14"><a href="#cb221-14" tabindex="-1"></a>                              presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb221-15"><a href="#cb221-15" tabindex="-1"></a>                              growth_lag <span class="sc">+</span> </span>
<span id="cb221-16"><a href="#cb221-16" tabindex="-1"></a>                              inflate_lag <span class="sc">+</span> <span class="fu">tt</span>(inflate_lag) <span class="sc">+</span>  </span>
<span id="cb221-17"><a href="#cb221-17" tabindex="-1"></a>                              loggdppc_lag <span class="sc">+</span> tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag), </span>
<span id="cb221-18"><a href="#cb221-18" tabindex="-1"></a>                     <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb221-19"><a href="#cb221-19" tabindex="-1"></a><span class="do">## f(t) = sqrt(t)</span></span>
<span id="cb221-20"><a href="#cb221-20" tabindex="-1"></a>wia_m5nph3 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb221-21"><a href="#cb221-21" tabindex="-1"></a>                     <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb221-22"><a href="#cb221-22" tabindex="-1"></a>                              womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb221-23"><a href="#cb221-23" tabindex="-1"></a>                              presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb221-24"><a href="#cb221-24" tabindex="-1"></a>                              growth_lag <span class="sc">+</span> </span>
<span id="cb221-25"><a href="#cb221-25" tabindex="-1"></a>                              inflate_lag <span class="sc">+</span> <span class="fu">tt</span>(inflate_lag) <span class="sc">+</span>  </span>
<span id="cb221-26"><a href="#cb221-26" tabindex="-1"></a>                              loggdppc_lag <span class="sc">+</span> tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag), </span>
<span id="cb221-27"><a href="#cb221-27" tabindex="-1"></a>                     <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb221-28"><a href="#cb221-28" tabindex="-1"></a></span>
<span id="cb221-29"><a href="#cb221-29" tabindex="-1"></a>model_list_m5nph <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m5nph1, wia_m5nph2, wia_m5nph3)</span></code></pre></div>
<div class="sourceCode" id="cb222"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb222-1"><a href="#cb222-1" tabindex="-1"></a><span class="fu">lapply</span>(model_list_m5nph, extractAIC)</span></code></pre></div>
<pre><code>## [[1]]
## [1]  11.0000 467.6995
## 
## [[2]]
## [1]  12.0000 467.8599
## 
## [[3]]
## [1]  12.0000 467.3552
## 
## [[4]]
## [1]  12.0000 467.6931</code></pre>
<p>Model with log(time) fits the data better.</p>
<div class="sourceCode" id="cb224"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb224-1"><a href="#cb224-1" tabindex="-1"></a>model_list_m45 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m4, wia_m4nph1, wia_m5, wia_m5nph2)</span>
<span id="cb224-2"><a href="#cb224-2" tabindex="-1"></a>model_list_m45 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_m45, add_AIC)</span></code></pre></div>
<div class="sourceCode" id="cb225"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb225-1"><a href="#cb225-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_m45,</span>
<span id="cb225-2"><a href="#cb225-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_m45, get_clse_hr),</span>
<span id="cb225-3"><a href="#cb225-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb225-4"><a href="#cb225-4" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb225-5"><a href="#cb225-5" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb225-6"><a href="#cb225-6" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Model 4&quot;</span>, <span class="st">&quot;NPH&quot;</span>, <span class="st">&quot;Model 5&quot;</span>, <span class="st">&quot;NPH&quot;</span>),</span>
<span id="cb225-7"><a href="#cb225-7" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Crisis&quot;</span>, <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb225-8"><a href="#cb225-8" tabindex="-1"></a>                               <span class="st">&quot;Woman HoG&quot;</span>, <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb225-9"><a href="#cb225-9" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified&quot;</span>,</span>
<span id="cb225-10"><a href="#cb225-10" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;Inflation x f(time)&quot;</span>,</span>
<span id="cb225-11"><a href="#cb225-11" tabindex="-1"></a>                               <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb225-12"><a href="#cb225-12" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Trade Openness x f(time)&quot;</span>,</span>
<span id="cb225-13"><a href="#cb225-13" tabindex="-1"></a>                               <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb225-14"><a href="#cb225-14" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>, </span>
<span id="cb225-15"><a href="#cb225-15" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B11: Banking Crisis and Women&#39;s Initial Access to the Finance Ministry, 1972-2017 -- Relaxing the PH Assumption&quot;</span>,</span>
<span id="cb225-16"><a href="#cb225-16" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB11.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B11: Banking Crisis and Women’s Initial Access to the
Finance Ministry, 1972-2017 – Relaxing the PH Assumption</strong>
</caption>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="4">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
Model 4
</td>
<td>
NPH
</td>
<td>
Model 5
</td>
<td>
NPH
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis
</td>
<td>
1.274<sup>***</sup>
</td>
<td>
1.295<sup>***</sup>
</td>
<td>
1.130<sup>**</sup>
</td>
<td>
1.186<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.391)
</td>
<td>
(0.391)
</td>
<td>
(0.448)
</td>
<td>
(0.441)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
</td>
<td>
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.051<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman HoG
</td>
<td>
</td>
<td>
</td>
<td>
0.625
</td>
<td>
0.667
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.554)
</td>
<td>
(0.544)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
</td>
<td>
</td>
<td>
0.288
</td>
<td>
0.295
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.301)
</td>
<td>
(0.301)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
</td>
<td>
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.898<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.245)
</td>
<td>
(0.246)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified
</td>
<td>
</td>
<td>
</td>
<td>
-0.054
</td>
<td>
-0.070
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.294)
</td>
<td>
(0.294)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
0.020
</td>
<td>
0.022
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.051<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.023)
</td>
<td>
(0.022)
</td>
<td>
(0.022)
</td>
<td>
(0.020)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
-0.005
</td>
<td>
-0.003
</td>
<td>
-0.020
</td>
<td>
1.087<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.034)
</td>
<td>
(0.034)
</td>
<td>
(0.104)
</td>
<td>
(0.387)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation x f(time)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.400<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.137)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
0.036
</td>
<td>
0.062
</td>
<td>
-0.374<sup>**</sup>
</td>
<td>
-0.397<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.117)
</td>
<td>
(0.119)
</td>
<td>
(0.183)
</td>
<td>
(0.185)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
-0.0002
</td>
<td>
0.007<sup>***</sup>
</td>
<td>
0.001
</td>
<td>
0.0005
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness x f(time)
</td>
<td>
</td>
<td>
-0.0003<sup>***</sup>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.0001)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
0.022
</td>
<td>
0.008
</td>
<td>
-0.098
</td>
<td>
-0.081
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.115)
</td>
<td>
(0.115)
</td>
<td>
(0.147)
</td>
<td>
(0.148)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
72,380
</td>
<td>
72,380
</td>
<td>
53,805
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-303.186
</td>
<td>
-300.798
</td>
<td>
-222.850
</td>
<td>
-221.678
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
618.373
</td>
<td>
615.596
</td>
<td>
467.699
</td>
<td>
467.355
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="4" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
</div>
</div>
<div id="corporate-quotas" class="section level2">
<h2>Corporate Quotas</h2>
<p>Estimate models that include corporate quotas as an additional
control variable.</p>
<div class="sourceCode" id="cb226"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb226-1"><a href="#cb226-1" tabindex="-1"></a>wia_m5_q1 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb226-2"><a href="#cb226-2" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> quota <span class="sc">+</span> </span>
<span id="cb226-3"><a href="#cb226-3" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb226-4"><a href="#cb226-4" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb226-5"><a href="#cb226-5" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb226-6"><a href="#cb226-6" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag))</span>
<span id="cb226-7"><a href="#cb226-7" tabindex="-1"></a>wia_m5_q2 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb226-8"><a href="#cb226-8" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> hard_quota <span class="sc">+</span> </span>
<span id="cb226-9"><a href="#cb226-9" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb226-10"><a href="#cb226-10" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb226-11"><a href="#cb226-11" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb226-12"><a href="#cb226-12" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag))</span>
<span id="cb226-13"><a href="#cb226-13" tabindex="-1"></a>wia_m5_q3 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, </span>
<span id="cb226-14"><a href="#cb226-14" tabindex="-1"></a>                 <span class="fu">update</span>(base_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> comp_hard_quota <span class="sc">+</span> </span>
<span id="cb226-15"><a href="#cb226-15" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb226-16"><a href="#cb226-16" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb226-17"><a href="#cb226-17" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb226-18"><a href="#cb226-18" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag))</span>
<span id="cb226-19"><a href="#cb226-19" tabindex="-1"></a></span>
<span id="cb226-20"><a href="#cb226-20" tabindex="-1"></a>model_list_tblB1 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m5_q1, wia_m5_q2, wia_m5_q3)</span>
<span id="cb226-21"><a href="#cb226-21" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb226-22"><a href="#cb226-22" tabindex="-1"></a>model_list_tblB1 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB1, add_AIC)</span></code></pre></div>
<div id="table-b1" class="section level3">
<h3>Table B1</h3>
<p>Show hazard rate coefficients.</p>
<div class="sourceCode" id="cb227"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb227-1"><a href="#cb227-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB1,</span>
<span id="cb227-2"><a href="#cb227-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB1, get_clse_hr),</span>
<span id="cb227-3"><a href="#cb227-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb227-4"><a href="#cb227-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb227-5"><a href="#cb227-5" tabindex="-1"></a>                               <span class="st">&quot;Corporate Quota&quot;</span>,</span>
<span id="cb227-6"><a href="#cb227-6" tabindex="-1"></a>                               <span class="st">&quot;Hard Quota&quot;</span>,</span>
<span id="cb227-7"><a href="#cb227-7" tabindex="-1"></a>                               <span class="st">&quot;Comp Hard Quota&quot;</span>,</span>
<span id="cb227-8"><a href="#cb227-8" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb227-9"><a href="#cb227-9" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb227-10"><a href="#cb227-10" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb227-11"><a href="#cb227-11" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb227-12"><a href="#cb227-12" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb227-13"><a href="#cb227-13" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb227-14"><a href="#cb227-14" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb227-15"><a href="#cb227-15" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B1: Women’s Initial Access to FM, 1972-2017: Controlling for Corporate Quota&quot;</span>,</span>
<span id="cb227-16"><a href="#cb227-16" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb227-17"><a href="#cb227-17" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB1.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B1: Women’s Initial Access to FM, 1972-2017: Controlling
for Corporate Quota</strong>
</caption>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="4">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.130<sup>**</sup>
</td>
<td>
1.087<sup>**</sup>
</td>
<td>
1.099<sup>**</sup>
</td>
<td>
1.118<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.448)
</td>
<td>
(0.449)
</td>
<td>
(0.445)
</td>
<td>
(0.452)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Corporate Quota
</td>
<td>
</td>
<td>
0.457
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.593)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Hard Quota
</td>
<td>
</td>
<td>
</td>
<td>
0.301
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.638)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Comp Hard Quota
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.105
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.939)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.050<sup>***</sup>
</td>
<td>
0.050<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
0.625
</td>
<td>
0.568
</td>
<td>
0.589
</td>
<td>
0.610
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.554)
</td>
<td>
(0.600)
</td>
<td>
(0.593)
</td>
<td>
(0.608)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.288
</td>
<td>
0.286
</td>
<td>
0.286
</td>
<td>
0.289
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.301)
</td>
<td>
(0.304)
</td>
<td>
(0.303)
</td>
<td>
(0.302)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.877<sup>***</sup>
</td>
<td>
0.879<sup>***</sup>
</td>
<td>
0.886<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.245)
</td>
<td>
(0.243)
</td>
<td>
(0.243)
</td>
<td>
(0.243)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.054
</td>
<td>
-0.041
</td>
<td>
-0.050
</td>
<td>
-0.052
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.294)
</td>
<td>
(0.298)
</td>
<td>
(0.296)
</td>
<td>
(0.297)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.046<sup>**</sup>
</td>
<td>
0.045<sup>**</sup>
</td>
<td>
0.044<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.022)
</td>
<td>
(0.021)
</td>
<td>
(0.022)
</td>
<td>
(0.022)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
-0.020
</td>
<td>
-0.014
</td>
<td>
-0.016
</td>
<td>
-0.018
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.104)
</td>
<td>
(0.103)
</td>
<td>
(0.104)
</td>
<td>
(0.105)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.374<sup>**</sup>
</td>
<td>
-0.396<sup>**</sup>
</td>
<td>
-0.387<sup>**</sup>
</td>
<td>
-0.377<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.183)
</td>
<td>
(0.187)
</td>
<td>
(0.187)
</td>
<td>
(0.184)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
0.001
</td>
<td>
0.0004
</td>
<td>
0.0004
</td>
<td>
0.0005
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.098
</td>
<td>
-0.093
</td>
<td>
-0.094
</td>
<td>
-0.096
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.147)
</td>
<td>
(0.149)
</td>
<td>
(0.149)
</td>
<td>
(0.150)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
53,805
</td>
<td>
53,805
</td>
<td>
53,805
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-222.850
</td>
<td>
-222.543
</td>
<td>
-222.735
</td>
<td>
-222.841
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
467.699
</td>
<td>
469.086
</td>
<td>
469.469
</td>
<td>
469.682
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="4" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
</div>
</div>
<div id="starting-time" class="section level2">
<h2>Starting Time</h2>
<p>We estimate models using alternative starting times (1946 and
1966).</p>
<div class="sourceCode" id="cb228"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb228-1"><a href="#cb228-1" tabindex="-1"></a><span class="do">## 1946 as starting</span></span>
<span id="cb228-2"><a href="#cb228-2" tabindex="-1"></a>s1946_fml <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(fwfm46_t0, fwfm46_t, fwfm46_d) <span class="sc">~</span>  .)</span>
<span id="cb228-3"><a href="#cb228-3" tabindex="-1"></a><span class="do">## 1966 as starting</span></span>
<span id="cb228-4"><a href="#cb228-4" tabindex="-1"></a>s1966_fml <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(fwfm66_t0, fwfm66_t, fwfm66_d) <span class="sc">~</span>  .)</span>
<span id="cb228-5"><a href="#cb228-5" tabindex="-1"></a></span>
<span id="cb228-6"><a href="#cb228-6" tabindex="-1"></a><span class="do">## Estimate 46 and 66</span></span>
<span id="cb228-7"><a href="#cb228-7" tabindex="-1"></a>wia_m5_46 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(s1946_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb228-8"><a href="#cb228-8" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb228-9"><a href="#cb228-9" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb228-10"><a href="#cb228-10" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb228-11"><a href="#cb228-11" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag), </span>
<span id="cb228-12"><a href="#cb228-12" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> iso3c, <span class="at">ties=</span><span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>))</span>
<span id="cb228-13"><a href="#cb228-13" tabindex="-1"></a>wia_m5_66 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(s1966_fml, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> </span>
<span id="cb228-14"><a href="#cb228-14" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb228-15"><a href="#cb228-15" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb228-16"><a href="#cb228-16" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb228-17"><a href="#cb228-17" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag), </span>
<span id="cb228-18"><a href="#cb228-18" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> iso3c, <span class="at">ties=</span><span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>))</span>
<span id="cb228-19"><a href="#cb228-19" tabindex="-1"></a>model_list_tblB2 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m5_66, wia_m5_46)</span>
<span id="cb228-20"><a href="#cb228-20" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb228-21"><a href="#cb228-21" tabindex="-1"></a>model_list_tblB2 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB2, add_AIC)</span></code></pre></div>
<div id="table-b2" class="section level3">
<h3>Table B2</h3>
<p>Show hazard rate coefficients.</p>
<div class="sourceCode" id="cb229"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb229-1"><a href="#cb229-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB2,</span>
<span id="cb229-2"><a href="#cb229-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB2, get_clse_hr),</span>
<span id="cb229-3"><a href="#cb229-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb229-4"><a href="#cb229-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb229-5"><a href="#cb229-5" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb229-6"><a href="#cb229-6" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb229-7"><a href="#cb229-7" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb229-8"><a href="#cb229-8" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb229-9"><a href="#cb229-9" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb229-10"><a href="#cb229-10" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb229-11"><a href="#cb229-11" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B2: Women’s Initial Access to FM: Alternative Starting Time&quot;</span>,</span>
<span id="cb229-12"><a href="#cb229-12" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb229-13"><a href="#cb229-13" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB2.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B2: Women’s Initial Access to FM: Alternative Starting
Time</strong>
</caption>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="3">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.130<sup>**</sup>
</td>
<td>
1.078<sup>**</sup>
</td>
<td>
1.057<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.448)
</td>
<td>
(0.427)
</td>
<td>
(0.443)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.050<sup>***</sup>
</td>
<td>
0.050<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
<td>
(0.012)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
0.625
</td>
<td>
0.676
</td>
<td>
0.645
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.554)
</td>
<td>
(0.553)
</td>
<td>
(0.549)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.288
</td>
<td>
0.385
</td>
<td>
0.383
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.301)
</td>
<td>
(0.296)
</td>
<td>
(0.290)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.914<sup>***</sup>
</td>
<td>
0.879<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.245)
</td>
<td>
(0.244)
</td>
<td>
(0.239)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.054
</td>
<td>
-0.053
</td>
<td>
-0.080
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.294)
</td>
<td>
(0.303)
</td>
<td>
(0.300)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.055<sup>**</sup>
</td>
<td>
0.048<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.022)
</td>
<td>
(0.024)
</td>
<td>
(0.023)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
-0.020
</td>
<td>
-0.028
</td>
<td>
-0.046
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.104)
</td>
<td>
(0.103)
</td>
<td>
(0.113)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.374<sup>**</sup>
</td>
<td>
-0.391<sup>**</sup>
</td>
<td>
-0.403<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.183)
</td>
<td>
(0.181)
</td>
<td>
(0.189)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
0.001
</td>
<td>
0.0002
</td>
<td>
-0.0004
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.098
</td>
<td>
-0.071
</td>
<td>
-0.050
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.147)
</td>
<td>
(0.142)
</td>
<td>
(0.142)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
53,805
</td>
<td>
53,805
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-222.850
</td>
<td>
-216.574
</td>
<td>
-199.407
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
467.699
</td>
<td>
455.147
</td>
<td>
420.813
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="3" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
</div>
</div>
<div id="alternative-methods-for-ties" class="section level2">
<h2>Alternative Methods for Ties</h2>
<p>We used Efron method in our main model. We will see if the results
are robust to an alternative method (Breslow’s method).</p>
<div class="sourceCode" id="cb230"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb230-1"><a href="#cb230-1" tabindex="-1"></a>cox_br1 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;breslow&quot;</span>))</span>
<span id="cb230-2"><a href="#cb230-2" tabindex="-1"></a>cox_br2 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m2, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;breslow&quot;</span>))</span>
<span id="cb230-3"><a href="#cb230-3" tabindex="-1"></a>cox_br3 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m3, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;breslow&quot;</span>))</span>
<span id="cb230-4"><a href="#cb230-4" tabindex="-1"></a>cox_br4 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m4, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;breslow&quot;</span>))</span>
<span id="cb230-5"><a href="#cb230-5" tabindex="-1"></a>cox_br5 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m5, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;breslow&quot;</span>))</span>
<span id="cb230-6"><a href="#cb230-6" tabindex="-1"></a></span>
<span id="cb230-7"><a href="#cb230-7" tabindex="-1"></a>model_list_tblB3 <span class="ot">&lt;-</span> <span class="fu">list</span>(cox_br1, cox_br2, cox_br3, cox_br4, cox_br5)</span>
<span id="cb230-8"><a href="#cb230-8" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb230-9"><a href="#cb230-9" tabindex="-1"></a>model_list_tblB3 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB3, add_AIC)</span></code></pre></div>
<div id="table-b3" class="section level3">
<h3>Table B3</h3>
<p>Show hazard rate coefficients.</p>
<div class="sourceCode" id="cb231"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb231-1"><a href="#cb231-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB3,</span>
<span id="cb231-2"><a href="#cb231-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB3, get_clse_hr),</span>
<span id="cb231-3"><a href="#cb231-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb231-4"><a href="#cb231-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb231-5"><a href="#cb231-5" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb231-6"><a href="#cb231-6" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb231-7"><a href="#cb231-7" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb231-8"><a href="#cb231-8" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb231-9"><a href="#cb231-9" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb231-10"><a href="#cb231-10" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb231-11"><a href="#cb231-11" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B3: Women’s Initial Access to FM, 1972-2017: Breslow’s Method&quot;</span>,</span>
<span id="cb231-12"><a href="#cb231-12" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb231-13"><a href="#cb231-13" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB3.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B3: Women’s Initial Access to FM, 1972-2017: Breslow’s
Method</strong>
</caption>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="5">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
<td>
(5)
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.040<sup>***</sup>
</td>
<td>
1.062<sup>***</sup>
</td>
<td>
0.862<sup>**</sup>
</td>
<td>
1.274<sup>***</sup>
</td>
<td>
1.134<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.359)
</td>
<td>
(0.386)
</td>
<td>
(0.374)
</td>
<td>
(0.390)
</td>
<td>
(0.446)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
</td>
<td>
0.055<sup>***</sup>
</td>
<td>
</td>
<td>
</td>
<td>
0.051<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.010)
</td>
<td>
</td>
<td>
</td>
<td>
(0.013)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
</td>
<td>
0.776<sup>*</sup>
</td>
<td>
</td>
<td>
</td>
<td>
0.622
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.464)
</td>
<td>
</td>
<td>
</td>
<td>
(0.551)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
</td>
<td>
</td>
<td>
0.608<sup>**</sup>
</td>
<td>
</td>
<td>
0.286
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.265)
</td>
<td>
</td>
<td>
(0.301)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
</td>
<td>
</td>
<td>
0.543<sup>***</sup>
</td>
<td>
</td>
<td>
0.887<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.129)
</td>
<td>
</td>
<td>
(0.245)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
</td>
<td>
</td>
<td>
-0.118
</td>
<td>
</td>
<td>
-0.054
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.266)
</td>
<td>
</td>
<td>
(0.294)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.020
</td>
<td>
0.044<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.023)
</td>
<td>
(0.022)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.005
</td>
<td>
-0.020
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.034)
</td>
<td>
(0.104)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.036
</td>
<td>
-0.374<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.117)
</td>
<td>
(0.182)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.0002
</td>
<td>
0.001
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.002)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.021
</td>
<td>
-0.100
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.115)
</td>
<td>
(0.146)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
85,092
</td>
<td>
68,792
</td>
<td>
67,641
</td>
<td>
72,380
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-369.234
</td>
<td>
-292.357
</td>
<td>
-287.929
</td>
<td>
-303.212
</td>
<td>
-222.922
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
740.469
</td>
<td>
590.714
</td>
<td>
583.858
</td>
<td>
618.423
</td>
<td>
467.844
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="5" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
</div>
<div id="figure-b1" class="section level3">
<h3>Figure B1</h3>
<div class="sourceCode" id="cb232"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb232-1"><a href="#cb232-1" tabindex="-1"></a>df_hr_br <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">pe =</span> <span class="cn">NA</span>, <span class="at">low =</span> <span class="cn">NA</span>, <span class="at">up =</span> <span class="cn">NA</span>)</span>
<span id="cb232-2"><a href="#cb232-2" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(model_list_tblB3)){</span>
<span id="cb232-3"><a href="#cb232-3" tabindex="-1"></a>  df_hr_br[i, ] <span class="ot">&lt;-</span> <span class="fu">extract_hr</span>(model_list_tblB3[[i]])</span>
<span id="cb232-4"><a href="#cb232-4" tabindex="-1"></a>}</span>
<span id="cb232-5"><a href="#cb232-5" tabindex="-1"></a></span>
<span id="cb232-6"><a href="#cb232-6" tabindex="-1"></a>df_hr_br <span class="ot">&lt;-</span> df_hr_br <span class="sc">|&gt;</span> </span>
<span id="cb232-7"><a href="#cb232-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">model =</span> <span class="fu">c</span>(<span class="st">&quot;Model 1: Bivariate</span><span class="sc">\n</span><span class="st">199 countries</span><span class="sc">\n</span><span class="st">75 women&quot;</span>, </span>
<span id="cb232-8"><a href="#cb232-8" tabindex="-1"></a>                   <span class="st">&quot;Model 2: + Woman</span><span class="sc">\n</span><span class="st">182 countries</span><span class="sc">\n</span><span class="st">65 women&quot;</span>, </span>
<span id="cb232-9"><a href="#cb232-9" tabindex="-1"></a>                   <span class="st">&quot;Model 3: + Institution</span><span class="sc">\n</span><span class="st">164 countries</span><span class="sc">\n</span><span class="st">63 women&quot;</span>, </span>
<span id="cb232-10"><a href="#cb232-10" tabindex="-1"></a>                   <span class="st">&quot;Model 4: + Economic</span><span class="sc">\n</span><span class="st">169 countries</span><span class="sc">\n</span><span class="st">64 women&quot;</span>, </span>
<span id="cb232-11"><a href="#cb232-11" tabindex="-1"></a>                   <span class="st">&quot;Model 5: Full</span><span class="sc">\n</span><span class="st">150 countries</span><span class="sc">\n</span><span class="st">55 women&quot;</span>), </span>
<span id="cb232-12"><a href="#cb232-12" tabindex="-1"></a>         <span class="at">mnum =</span> <span class="fu">seq</span>(<span class="dv">5</span>, <span class="dv">1</span>), </span>
<span id="cb232-13"><a href="#cb232-13" tabindex="-1"></a>         <span class="at">tie =</span> <span class="st">&quot;Breslow&quot;</span>)</span></code></pre></div>
<pre><code>## mutate: new variable &#39;model&#39; (character) with 5 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;mnum&#39; (integer) with 5 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;tie&#39; (character) with one unique value and 0% NA</code></pre>
<div class="sourceCode" id="cb236"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb236-1"><a href="#cb236-1" tabindex="-1"></a>df_hr <span class="sc">$</span> tie <span class="ot">&lt;-</span> <span class="st">&quot;Efron&quot;</span></span>
<span id="cb236-2"><a href="#cb236-2" tabindex="-1"></a>df_hr_tie <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(df_hr, df_hr_br)</span>
<span id="cb236-3"><a href="#cb236-3" tabindex="-1"></a></span>
<span id="cb236-4"><a href="#cb236-4" tabindex="-1"></a>df_hr <span class="sc">==</span> df_hr_br</span></code></pre></div>
<pre><code>##      pe   low    up model mnum   tie
## 1 FALSE FALSE FALSE  TRUE TRUE FALSE
## 2 FALSE FALSE FALSE  TRUE TRUE FALSE
## 3 FALSE FALSE FALSE  TRUE TRUE FALSE
## 4 FALSE FALSE FALSE  TRUE TRUE FALSE
## 5 FALSE FALSE FALSE  TRUE TRUE FALSE</code></pre>
<div class="sourceCode" id="cb238"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb238-1"><a href="#cb238-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> df_hr_tie, </span>
<span id="cb238-2"><a href="#cb238-2" tabindex="-1"></a>            <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> pe, <span class="at">xmin =</span> low, <span class="at">xmax =</span> up, </span>
<span id="cb238-3"><a href="#cb238-3" tabindex="-1"></a>                          <span class="at">y =</span> <span class="fu">reorder</span>(model, mnum), <span class="at">color =</span> tie)) <span class="sc">+</span> </span>
<span id="cb238-4"><a href="#cb238-4" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="dv">2</span>, <span class="at">alpha =</span> <span class="fl">0.4</span>) <span class="sc">+</span> </span>
<span id="cb238-5"><a href="#cb238-5" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> .<span class="dv">5</span>)) <span class="sc">+</span> </span>
<span id="cb238-6"><a href="#cb238-6" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Effect of banking crisis on survival time until first appointment&quot;</span>, </span>
<span id="cb238-7"><a href="#cb238-7" tabindex="-1"></a>       <span class="at">y =</span> <span class="cn">NULL</span>, </span>
<span id="cb238-8"><a href="#cb238-8" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Methods for</span><span class="sc">\n</span><span class="st">Handling Ties&quot;</span>) <span class="sc">+</span> </span>
<span id="cb238-9"><a href="#cb238-9" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span> </span>
<span id="cb238-10"><a href="#cb238-10" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="dv">16</span>,<span class="dv">1</span>)) <span class="sc">+</span> </span>
<span id="cb238-11"><a href="#cb238-11" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb238-12"><a href="#cb238-12" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">3</span>,<span class="dv">5</span>, <span class="dv">7</span>)) <span class="sc">+</span> </span>
<span id="cb238-13"><a href="#cb238-13" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="fu">guide_legend</span>(<span class="at">reverse =</span> <span class="cn">TRUE</span>)) <span class="sc">+</span> </span>
<span id="cb238-14"><a href="#cb238-14" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb238-15"><a href="#cb238-15" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))</span></code></pre></div>
<p><img 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gRA0yVJOwgggAACCCCAAAIIIIAAAggggAACCCAQOQECoJG7JZwQAggggAACCCCAAAIIIIAAAggggAACCKRLgABouiRpBwEEEEAAAQQQQAABBBBAAAEEEEAAAQQiJ0AANHK3hBNCAAEEEEAAAQQQQAABBBBAAAEEEEAAgXQJEABNlyTtIIAAAggggAACCCCAAAIIIIAAAggggEDkBAiARu6WcEIIIIAAAggggAACCCCAAAIIIIAAAgggkC4BAqDpkqQdBBBAAAEEEEAAAQQQQAABBBBAAAEEEIicAAHQyN0STggBBBBAAAEEEEAAAQQQQAABBBBAAAEE0iVQPl0N0Q4CCCCAQHoFVq9ebT788EPbaMOGDc1ee+2V3gPQGgIIIIAAAggggAACCCCAAAI5IEAANAduMpeIAALZKbB+/Xozd+5ce/IVKlTIzovgrBFAAAEEEEAAAQQQQAABBBAoYQG6wJfwDeDwCCCAAAIIIIAAAggggAACCCCAAAIIIJA5AQKgmbOlZQQQQAABBBBAAAEEEEAAAQQQQAABBBAoYQECoCV8Azg8AggggAACCCCAAAIIIIAAAggggAACCGROgABo5mxpGQEEEEAAAQQQQAABBBBAAAEEEEAAAQRKWIAAaAnfAA6PAAIIIIAAAggggAACCCCAAAIIIIAAApkTIACaOVtaRgABBBBAAAEEEEAAAQQQQAABBBBAAIESFiAAWsI3gMMjgAACCCCAAAIIIIAAAggggAACCCCAQOYECIBmzpaWEUAAAQQQQAABBBBAAAEEEEAAAQQQQKCEBQiAlvAN4PAIIIAAAggggAACCCCAAAIIIIAAAgggkDmB8plrmpYRQAABBIoiUKFCBVOrVi3bRNWqVYvSFPsigAACCCCAAAIIIIAAAgggkLMCBEBz9tZz4QggEHWB6tWrmy5dutjTrFy5ctRPl/NDAAEEEEAAAQQQQAABBBBAIJICWRsA/fXXX02fPn3yoB511FGmb9++edYl+zB27Fijlyt77LGHueGGG9zHEnn/8MMPzc0332yPfdppp5kTTzwx7ecxYcIEM3r0aNvugAEDTKdOnQp9jN9//90888wzZvbs2WbBggVm2bJlpkGDBqZhw4Zm9913N8cff7xRJltpKM8++6wZM2aMvZTDDz/c9OvXL+llDRw40MyZMyeo9/DDD5u6desGn8MW/GegXbt25rrrrgurxjoEEEAAAQQQQAABBBBAAAEEEEAAgSQCWRsA3bJli/ntt9/yXN4rr7xizj33XFO2bOpDm7700kt52lm9enWeNkviw6ZNm4JzWr9+fUZOYcOGDcExNm7cWOhjPPfcc2bUqFFmzZo1edr44YcfjF7vvfeemThxohk0aJDZc88989TJxg+tWrUK3KZPn570EvQ8ffzxx+avv/4K6n700UfmmGOOCT6HLXz66afBcZo3bx5WhXUIIIAAAggggAACCCCAAAIIIIAAAikIpB4pTKGxkq6yfPly89VXX6V8GvPmzbNBupR3oGIegddee83cfffdQfBz3333tQHoK6+80vTq1cs0btzY1v/555+NsiDnz5+fZ/9s/LDzzjsb1xVZ15MsYP7JJ5/kCX7qmpXdmazMmDEjqLLPPvsEyywggAACCCCAAAIIIIAAAggggAACCBRMoFQEQCtVqhRc9ZtvvhksJ1t44403klVhexyBX375xdx+++12a7ly5czQoUPt59NPP90ceeSRdngCdbE/4YQTbB1lmWpogc2bN8dpMTtWly9f3miYBFdmzpzpFkPfXbBz2223DfZTdmcih3Xr1gVd5mvUqGGaNm0a2jYrEUAAAQQQQAABBBBAAAEEEEAAAQSSC5SKAOiOO+5ox5zU5b7zzjv5Mu7iMbgA6K677hqvCuvjCMjOdc8/9dRTzUEHHZSvpoKFF110kVG3cZW5c+facULzVczgCgVkdX4jRoxI21H22muvoK1kGcfq7q6ioKnL5FSAM9F+2ua6zO+9996mTJkywfFYQAABBBBAAAEEEEAAAQQQQAABBBAomECpCIDqkg899FB75eoG/+WXXyZV+Pbbb83ChQttvc6dOyetT4W8An4X7UTjWSo71J9gyZ8MKG+Lmfmk7vd6aWKmdBU/AJroWVPAV8+jisY/dQFQfXaBUS3Hli+++CJYpQAoBQEEEEAAAQQQQAABBBBAAAEEEECg8AJZOwlS7CUfcsgh5sknn7Sr1Q3e76YcW1efp0yZYlfXqVPHtGnTJqxK6Lo//vjDaAb1WbNm2fFDNRGTMlDVTblt27Z5gn2hDfy9Ut3BNWGTsig1a7ombdptt91skEzdxwtSvvvuO/P2228bvSvgptnWNWmOXl27djU1a9YsSHMp11XXbI2HqTEwk81o7p/D0qVL8x3j1VdfDYLWum+HHXZYvjpRWtGkSROj61+xYoX55ptvbHd2ZbvGFj/IqfFRGzZsaLbbbjuzcuVKOw5o3759Y3exn/3gcqIA6Ndff22fI0029eOPP5ptttnGPod6FnXva9WqFdq+Vj7wwAP23unZ1zOn+6Ls6c8//9yo3R122ME+z+3bt7f32TWk51X1PvvsM5vNq5+fZs2amTPPPDPIwnZ1Y981Zuq7775rfvrpJ3u+GkahevXq9liy0ZcYrVu3jt3NflagWc+JSu/eve1+Gl9VE0xpGAK12ahRI9OiRQtzxBFHBFnHdgf+QAABBBBAAAEEEEAAAQQQQACBnBbIH7XJUg4FYRQAUSBEAZqLL7447mzwmkH+rbfesleq7M9UZ41XYPW+++4zS5YsyaOk4IxeCozut99+5rLLLosbeNT5aUKgRYsW5Wlj8eLFRpMKadZ0l82ap0LMB3WRHjNmjJ2B/c8//8yzVYElGYwbN86ey4EHHphnezo+XHXVVSk3o8CsK2HjWSrg9+KLL9oq6u4d9QCoTrRdu3bm9ddft8Hs2bNnm1122cVdYvDuAqAKEOvZVFH2qILv33//vc1KjQ1SbtiwwQZVVVdWCrTGFgVeR44cGQQE3XYF45XtqoD4s88+a/75z3/aYKDb7r/rWVPQc9OmTTZYeMkllwTZqqqnzFVloj7xxBPmrrvusl8oKNCoZ3ft2rVBU7///rsNvutn46abbjIKmMYWPZ9PP/20efTRR+3x/O0KoOtnQuW5554zPXr0MAoMxwaUVWfSpEm23imnnGKXH3nkEfvZ/aHAsvs5vOCCC8yJJ57oNvGOAAIIIIAAAggggAACCCCAAAI5LFBqAqC6hwocauIdBYgUvFG347CiMRZdEDPV7u9Tp0411113XdCcsh/VvjLgFMyaNm2aDWjpXTOgjxo1Kl9mpIKcCswoaKSy/fbbmwMOOMBmzqlruLLjtL+fARgcMGZh8ODBwWziW221ldF1KOtT2aXKSlQgVQEhzcjer18/c9JJJ8W0UDwfNd6lgm2uxMvwc9uz5V2BTAVAVfQ8xQZA/XE+lf3pipZd9rECpEcffbTbZN+VfekmSPL3c5UUTLz88suDIOnWW29tn6GWLVuaNWvW2GzI6dOn2+xOBSQVfL7wwgvd7vnelZWp52PVqlU2aK9jasImZVfqudbx9NxrLNfbbrvNjvuqa1empX6GVE/PmQK3d955pw3K63n0i/ZXQF5FbR9++OH22a9YsaLRz4SeeR1LQf1nnnnGtq068YqCv3q+NbyCznennXay56KsVAVudc733HOPzcwOM4zXLusRQAABBBBAAAEEEEAAAQQQQKB0CpSqAOjBBx9sA6C6VcrwjBcAdQEoZeUpaOhnKIbdZgUsb731VrtJQZfzzz8/X0BRmWwKOCmYo2CSJt25+eab8zSnwI0Lfior85prrjGVK1cO6pxxxhnmiiuusMGgYGXIgs7fzS6uLMEbb7zRdq/2qyqIqvYVnFKmnILDsdmGfv1MLT/22GNGmYkqGgOzQYMG+Q6l7v+uZMuEVMoAdUVZhyeffLL7aN8VjHOBTH/sT79Le1gA1A9++3Vd48r6VYBbRUHPG264IZ+pMkCHDRtmFIRVJqiC46obVjQWroqC9n369Amq6NyVyawu5vpCYciQIbaLvbJB/WvX2KraT+/KalbwVUF9V1xGtj7r501d7xUE9Yv21zOqbFMVnXOiAKiCnwp66pz07orO+dJLL7Xd87Xu+eeftwFSt513BBBAAAEEEEAAAQQQQAABBBDITYFSFQBVMEQvZbXF6wav7DC/+3sqt13BTBfE6969e77gp9pQUGfo0KGmZ8+eRl3QFaRRMFRd4lU0RqM7rsaBVFacMuD8Ur9+fRtEVRtuFnB/u5Y1Bun9999vVyvTTgEwjZ8YWzSWpgKgCgi5ffS5OMtLL71kxo4daw9ZpUoVG1ALO77GoCzo2Kd+O/J2wTN/vZZ1v1UU6Lvlllvssv+Hxs1UBmRBi7q1y11dzsNmdHfd3xUw9wOGGg9VQWtlPCp7UuenOq64AKieDT8wrO0//PBDEOCvVKmSzcgM6yKvSafUTV3Xq+fojjvuMA8++GDc2eQ7dOhgx9V056B3dUE/9dRTbQDUrT/rrLPyXIvWK6iu9cOHD7fV3MRibh8Nw+BK//798wU/tU3DHqgNdYFfv369/UJCw1RofVjRemU2+8FP1dM560sITcqlYKjuQaJ2wtqO2jpltLqu//qyRmMdUxBAAAEEEEAAAQQQQAABBBBAoGACZQtWPfq13fiZrht87BlrkhdlRaq4urF1Yj+7bEtla55++umxm4PPCsBoghZXXBdpff7ggw/cajs2YWzw023U5DOJghzqiu+673fp0iUYW9Lt778r87Bx48Z2lbJGNd5jcRWdp7pMu6LgV7LJklzdgr4rOD158uTQl2tLXa3D6vj3yNVN9d3NBq/nSYFQv7gAqIZKUJDVLy4jVFnDLptT23V/1AVeRQHs2K7kLmCq7RorMyz4qW0qejY0OZeKjhF7fnbDf/9QwD0s2Oj2d3W7devmFvO8+wF4OfvluOOOsxnK6oYfltHq6upnx2UHKyic6FnVZGOtWrVyu+Z51xcR7plXO/54pXkqZskHXYMyyvVSRi8FAQQQQAABBBBAAAEEEEAAAQQKLlDqAqB+8NBlXPosrvu7xjB0E9P422OXFVxToEpF4zxWq1Yttkqez/vvv3/wWVmfrijw6kqyGepd1qir77/7gaxUuosrAKeiTMDYiZf8dtO5rMxPTZKk4I2KuvbHjnWZzuOVVFsuAKrj+1mg6vbtrMPGoHQBUO2n7vOuKFCpMVxVwoKFbrIgbVfWZqKirFJ/QiJ/39j94nWPr1q1alBVmZ7xgvbKRnUlNnCpLE1lpGqIgHiTjan7vALR7osJteWeHdeu/66xcxMV/7xjzyfRfmxDAAEEEEAAAQQQQAABBBBAAIHSKVCqusDrFikbTV1Fv/vuO6OxEP3Z4BUM0URDKqlOfuQHMdVFPVlRV28FYJSxpWCl64KrrqyuaOKkRCXR9gULFgS7akZ6dW1OVPwMOJ1PKkHfRO0l26bJnzQRlSvq2nz22We7jxl5V2Ba9zqsKCCu4O9BBx1khwsIq1PYdRpjVkE9ta9A5lFHHWWbctmf+uAHO91x1LVdwURNHOQyPrVNE3e5ErafusC7ksqz6NeJFwCtUKGC8QOYrn29+1mhicaPjRfY9NvSssa/VaBYP1N6FvXSefmBT7ePfm7ilXr16sXbZNfrmlxJ1I6rwzsCCCCAAAIIIIAAAggggAACCJRugVIXANXtUtd2BUCVvenPBu9mx1YdP1NUn+MVBWlcSRSYdHX0Xrt2bRsA1dibCvpozE83hqi2axzIRCXRcfwAaFjgKFG7GiszU0VjLmocSGV/qigoNmDAABOv23S6z8MfR9Nv2wXxdD7x6vj1C7Ks7tbKnlTmpp8B6gKgyhYOy65U13YFTzW0gh8AdeN/KtgYO76lzss9i7qOZM+Q6us5dCW2a7pbHy/46ba791SDnK6+//7rr7/aMVpfeeWVIMPV365lPfMK1vsB+9g67rNmvqcggAACCCCAAAIIIIAAAggggAACqQqUygCoZoPXbNMqb775ZjAb/BtvvGHXKQMv1fEo/e60a9assfsn+0PZn664YI0yQ11ROwqKxiuJuv/6s8YruzJZl3z/GKl0mffrp7qsoNXVV19tJ/XRPgqqXX/99cYfDiDVtrKtnrrBKwCqTEYNlaDMTjfcgbqxxwscKsNTAVBlBitAqGDlzJkz7eWHZX9qg55FdRfX86HJgvxnKszNfw5jxyF19eOdn9te1Hedr8b/1DW6osBxkyZNTLNmzeyEUK1bt7bLGj939uzZtpoLXLt9eEcAgdwVUJa9fpdo/GuNhasv3PS7Vr9LlBHu/z2du0pcOQIIIIAAAggggAACCCQSKJUBUE2moqCKAlOaDf6SSy6xk6q8//771iLV7u+qrEmJXHHjOrrPYe/6j5n+o6ai4KfrjquMPdeFWcGgRAHQeNl6alNd/DUZjorG9/THebQri/kPZaFqmAHNaq6i67z11ltDMx+L+dSK5XAKgD755JP2WJppXpP5uHE84wUyVdnfpixQDU3gJrkJG/9T++hZnDdvnhbtGKOaTT5R8Z/XRM9bojaKuu3aa68Ngp/Khh00aFDcZ8PP/qTrelHl2R+B7BfQ7wF9uaTfrepREVb097z+3tFQKNWrVw+rwjoEEEAAAQQQQAABBBBAwJS6SZDcPXVd3F03eGXbKWtO3Yc7/T0pS6rFD4DOnz/fjumZaF8FqFzwxt9X2W6uqJ1EZeHChXE3+zNuu2y5uJX/3jBnzhzbzXrFihXBeSWqX5BtynhUN3cX/FS3bY1JGtbtuyDtZlPdNm3aBLO16z/irhu7riFeIFPbNMO6G+pAAVDXhV6Zj/7kSqrrin/vXSDUbQt7d/dF25JNHBS2f1HXKQPVXZeyVe+99964z4a+OPDHyU2UBV3U82J/BBCIvoB+J2jYGv1OjRf8dFeh3x3vvfde8AWRW887AggggAACCCCAAAIIIOAESm0AVN3gXVEWqLrCqyi4VJAsEXWzc8FLjcEYb7IddyyXDajPBx54oFttOnbsGCyPHTs2bjBSwdPx48cHdWMX3KzuWj9hwoQgazC2nj5rkp2BAweavn372rE4XQZqWN3CrLvjjjvsWKvat1WrVjbAlerQAoU5XhT30XieGlJBRVlKbiIjZWcmmjhI9d0M8X4AtEWLFnGzg5Xh5MqYMWPs5Evuc+y7gp9Tp061qzVsQrygaux+6fysgLArChQnGm9U56ovKFwhAOokeEcg9wTU5V1jKSfqDRGror879YWL/8VPbB0+I4AAAggggAACCCCAQO4KlNoAqAJxbsxLBUA/+OADe5c1QVJBS79+/YJdlOG4dOnS4LO/oCCOC5Cq6/thhx0WbFYASLPTqyh7b9y4ccE2f0FBzUQZogqCueCusl7uueceOyak34ZbHjlyZDD5kgJgYRPruLoFfVc3fDemqrof3nLLLYUeh03/yVXwUC9/rMiCnlNY/WHDhtku+aeddlrY5rSsc8HFWbNm/R97ZwJv1dT+8ZWhKCpJI2kyJEpFg6gQFSEaDBkqEaHMEVIkU5JkzFApYyGZxwhvMoQyFpJoTimRofu/3/V/137XPfecc8907z3D7/l8zt377L322mt99xnu+e1nCIoaOXEz2glcGDxeuk44ddvCHYeg3rRpU7uL1xBCejgjlH7s2LHBLlI+IOSXtPlFmKj8/vfff4cdwueff26uv/76AvtcGoECG/VEBEQgJwhw88Slkol3wuRSJuJBJgIiIAIiIAIiIAIiIAIiIAI+gazMAeomSBg8HiHuhxTeeu3atXO7Y162aNHChs0jbhKeTvEhRNHmzZvb3GN4Vr766qvm0UcfDfo899xzDblInRHaPGTIEHP22Wdbz71x48bZqt7HH3+8DYfGu3TGjBnmqaeecodEXFJU5v3337cens8//7xBXDrrrLMM3oN42TEePARff/112wfiF+dNlSFkjRkzJuiO8GbyfsZiCHw9evQo0HTSpEmGeWBUjcdrNVXWpk2bVHUVsR8ngPoh3NGETNcRryuKEMGT4h5YtLB59pNv9cwzz7SvIQp9LVq0yPTs2dN6KeNByY//u+66K6gYT4GQgQMHcmiJG2H+VapUsWIEwjb5QLt162aLkpErdeHChbZg1MMPP2xfy/4A169fH6QI8LdrXQREILsJcAMnWS9OPgcT+a7PbrKanQiIgAiIgAiIgAiIgAjkNoGsFkDJ9YnQ6HJyUjDIVWWP97IjytEP3qSIMzfccIPtgpyifrguQicCKaJUqJEbc8SIEdbbDQ83vD15+H0g0iKu+gJjaD94tyI4jho1ygpniLyDBg0KOx76HjlypC0KFdpPos8/+eSTQGCjD8RbHrEYgli2GZ69VCF2VdcRoV1YfLS5UsGYYl2EwGOEqjuv5UjHkY6B19Do0aPt6xCRm4f/GnLH1q1b175OI1WAd+2Ka8mYrr76apsnlnNQhIwH80T4dUWPaHfKKafY1BSItxgeYM5j2m7I0T/cXHBpL0ojj2uOYte0S5FALLm2ixoeub95uCKERbXXfhEQAREQAREQAREQAREQgewnkLUh8Fw6cjC6kGGex1P9nfa+VapUyQqJeLH5np1O/ETQOeigg2weTATQSIYoS2i6X73d9UHRJIStWLwW8R4k3+ixxx5rENKcub4YT6dOnWwb/1yuXTLLaCH6yfSbqcfCmuvhDM/gWH94+56izZo1s1XkXT+RlryGJk+ebFMhICZi7rqzTnGlfv36mQkTJtjq8mwrLcOjlZsQeCc7w1MV8RMvUOb84IMPGjym/Zy5r7zyimue00vEa15PPBC0ZSKQ7QTiyfsZjUWq+ol2Du0TAREQAREQAREQAREQARHIHAJl8r0a8zJnuOkzUsL0yMNI6DJCKxW64ymuxEw4dsmSJTb8Fy+veI/3aRDmz3iolotAywPvMVl2E+Dtu2zZMhsyiiclIe8IZYiy6WSMkzB4Ukhs2LDBpn3gPYMIKotMAE9xl1oBsbty5cqRG5fgHq7nnDlzSvCM6XsqqpXDI9abHuk7k9IfGRwj5diOd3REU3BzkOuDcX3S7XMx3jllY3s+47gu+i5Iv6tLeh4KkmG8n4hwkqUXAYqdJvvZRo0C35EivWaYuaPhJhzfaXy+5VqB2Ey4ajhjrFu3zl4b/W+QCVdMYxSB1BGQ+pAgS8RF/mlIxvDU45EKQ4Qtqup4Ks6jPtKLAD9InOCdXiMrOBrGiTjLQ5b5BPin3uWtzfzZaAbZSMC/eZCN89OcREAERCAVBPislImACIiACIhArhBILzexXKGueYqACIiACIiACIiACIiACIiACIiACIiACIiACJQIAXmAlghmnUQEREAEsocA4ULJ5FTOHhLGFj8jB28yKUyyiUeyc5k1a1YQtp5MX3jG181PB7Jx40bbDSGeLmdyMv3q2NQSIMVHuXLlTGkV60vtbLKrN8JDnXfgzjvvbIstZtcMM3s2fO/w/qFGAe+hRI3CnTIREAEREAERyBUCEkBz5UprniIgAiKQQgIVKlRIYW+Z2xV58sgzKR6puYakhfnll1+S7gwBFMHT5QAlbY3yYieNNeUdUBAPAUbvn5SjTbpD8ku63Hi8d5SnNWmkKe0AAZT3D9dGImZK0aozERABERCBLCagEPgsvriamgiIgAiIgAhkEoFdd9016eEi1KQqv3bSg1EHIiACIiACIiACIiACIiACaUFAAmhaXAYNQgREQAREQAREoGbNmqZy5cpJgdhzzz1tZeSkOtHBIiACIiACIiACIiACIiACWUVAAmhWXU5NRgREQAREQAQym0CzZs0SDretUqWKadCgQWYD0OhFQAREQAREQAREQAREQARSTkACaMqRqkMREAEREAEREIFECVSsWNEceOCBcYugFANp2bJlkLcw0fPrOBEQAREQAREQAREQAREQgewjIAE0+66pZiQCIpAlBPLy8syff/5pH66YS5ZMTdMQgagEyOF5yCGH2ArHURv+d2edOnXMwQcfnFQ15FjOozYiIAIiIAIiIAIiIAIiIAKZSUBV4DPzumnUIiACOUBg1apVZtq0aXamjRo1Ml27ds2BWWuKIvD/BPAEbd++va0K/9NPPxneD1u2bAnwlCtXztSoUcPUq1cvZqE0OFgrIiACIiACIiACIiACIiACOUVAAmhOXe7ok50/f74577zzzI477mheeOGF6I3/u3fz5s3mySefNJ999pn58ccfzfr16w1VfHfffXcr1rRo0SKmfjjuiSeeMN98841ZunSpWb16talVq5bZbbfdTNOmTc0JJ5ygohYxkUys0bJlywzFR5Kx/v37m5UrV5oddtjBPProo8l0pWNFQAREwBIoU6aMqV27tn3gEc13zt9//2222247fSfoNSICIiACIiACIiACIiACIhAzAQmgMaPK7oYbNmwwN954o+EHZqw2b948c9NNN1nvHP+YhQsXGh6vv/66OeKII8zFF19sRTG/jb/+1FNPmYceeshs3LjR32wWL15sH7NnzzYzZswwl112maE4hix1BP744w8zadIk8/LLL5tnn302qY4RsX/99VcrTiTVkQ4WAREQgTAEEEMRPnnIREAEREAEREAEREAEREAERCAeAhJA46GVpW1///13K1ISYhirrVixwgwdOjQQLffee29z6KGHmp133tn88MMPVkyj39dee82Qu/C6664L2/Wrr75qxo0bF+xr1aqV9fisWrWq9QR95513rAjK2C699FLzwAMP2HDH4ACtJEXg/PPPN99++60h1FQmAiIgAiIgAiIgAiIgAiIgAiIgAiIgAtlIQAJoNl7VOOa0YMEC68VJ+Ho8duuttwbi5/HHH28GDx5stt5666CLHj16mIEDBxpCq9966y3rDdqxY8dgPyu//PKLGT16tN3GsYik7dq1K9Cmb9++Zvz48Wb69Onmr7/+sm0mTJgQd3XgAp3qSUDgt99+s+t4ViVrl1xyiS3Ws802+lhJlqWOFwEREAEREAEREAEREAEREAEREAERSB0BVYFPHcuM6onQ5zvuuMOKlPGKnwiRH330kZ0vlXovuOCCAuInO/DgJPTdGWHsofbGG28YxoGdcsophcRPtiOm0T8eptiiRYtsnlD7RH/SikDr1q1Nhw4dbCXmtBqYBiMCIiACIiACIiACIiACIiACIiACIpDTBCSA5uDl/+KLL8wZZ5xhq0uT8xPvv9NOO81UqVIlJhqEuP/777+2bePGjSMWoiBf51Zb/f9LjOJGofbpp58Gm4499thgPXQF71CENWeEbMtEQAREQAREQAREQAREQAREQAREQAREQAREIBYCilWNhVKWtXn//fdtaDrTImfnsGHDTPPmzW0hHLbFEw69adMmDglrW7ZsCYoqUbU31BBc99lnH0MBpurVq4fuLvCccTpbtWqVW01oiQD8/PPPW09ScpmWL1/eVq5v0qSJ6dWrl6lQoULUfjmeokEUacJ7lqrnDRo0sI+uXbta79dIHeB1iwftTjvtZKiaHsnwdH3mmWfs7oMOOsi0bdu2QNN7773XcoPf0UcfbdatW2fwqCWlAQ9E4z333NPst99+plu3boVE6jvvvNOGq1O4CMMTl7QG2G677WZOOukku06hK4pZYeecc44hr+uUKVPMf/7zH1uIpGXLlrYt1+/+++839EeBErx2Ixk5YV944QXz1VdfWY/e5cuXmzp16piGDRuaAw44IKwnsN8X4vusWbPsfH/++WfD6wFP5Hr16pm6deuazp07F/l68vvTugiIgAiIgAiIgAiIgAiIgAiIgAiIQHYTkACa3dc34uwoenPiiSea7t27Fyn4hXaCQFauXDmzefNmgxiI+Fa5cuXQZlYkc1Xl99hjj0L7r7rqqkLbIm1AEHSG2JiIIdbecMMNhsJKvpEHExGOsH4q0l955ZXmkEMO8ZvY9bVr15q7777bvPLKKwX2UfmcIk2Ick8++aTNh9qpU6cCbdwThD/Extq1a0cVQMmd+txzz9nDEH9DBVCKRyH8/fnnn1a8Jt3A0qVL3WnsEnGQ/Ku0Jb9qzZo1g/0vvvhikMOVjYiy7nz7779/IIDi7eu2k+v1iiuuMIjGzpYsWWKFS0RYCl7BEUE4kgCKaMxYFi5c6Lqwy/nz5xseiL6HHXaYLXi14447FmjDE15r9E0/viGif/fdd3bT448/btMvHHHEEX4TrYuACIiACIiACIiACIiACIiACIiACOQoAQmgOXjhEZh69+5tPR8TmT4ekyeccIJ57LHHrIg2cuRIc+211xpfsCLk/fbbb7fd41FKjs9EDeESEc9Zo0aN3GrMSzxQzzvvPOtxyEHbb7+9DavHSxKRFo/GDz/80HpVDh8+3OAhiXelM7wOEf/wWsTwEj344IPNXnvtZRngdTl37lx7PDwQbDlfcRui57nnnmvWrFljvVibNm1qKlWqZD7//HM7Vsb99ddfG+Z03333BcM56qijrIANVwTZsmXLmi5dutj9CNzh7K677grET1Ib4OGLEH7ooYeGa15oG96kl156qRVb2Qm7Vq1amRo1apjvv//efPDBB1ZIfvPNN62wPnHiRCumuo7wHOUaOPET71aEYcR3hFqOZ9/GjRutyIonajgh2/WnpQiIgAiIgAiIgAiIgAiIgAiIgAiIQG4QkACaG9e5wCwT9aD0Ozn77LOt6IaAhvCEwEn4MsWPCAtnGwIZRYwuu+wyQ3h5ooYQhpclRsh1rVq14u7qkUceCcRP5n/99dfbUG/XUc+ePQ2eg4h8eEPedttt5sEHH3S7bdi3Ez8R7vBiDB0HHqA33nijQbDFE5Sq97QtTvvyyy9t91yPk08+2fJ25+MaDBkyxOZrpR2itBuP89CkDQIogjDiZDTDQ5Y5Dx482F5rRF48PhHEizLEyzFjxgTi58CBA60HsssRy/F4FMP9pZdeskLrww8/XMCTFCEXj2MMz+ULL7zQrrs/559/vpk6daohPQCGt20yAiivX5fr1p2jpJdwc4ZQHy6VhNuvZekQcKk+dG1Kh3+0s/rvX9Z1jaLRKp19fK7p2pQO+6LOymebM76LXESP26Zl6RJwn29cG322le61iHZ2/e8WjU7p7XP/X/Pe2XbbbYOaFaU3Ip1ZBESgpAhIAC0p0ll2HoTNa665xhAuTe5IQpNdrkg3VTzzyHlZv359tynuJaHaeJpiCG2XX3553H0grD366KP2OLxRR4wYUUD8dB2S9/K9994zFGei0BJC7u677269ChHjMLwKb7nllrAFoyjURI7Mm266yYq/iHl4XcaTU9WNJZ4lOUIpYhVqeFc6YZd9c+bMCQTQ0LaxPGcepC1wYjYesr6XbLQ+ZsyYYTnSBu9TxNpQw5t06NChhrB6hM7p06cbimNxDTC8Wp21b9/erRZY9ujRwzz99NNWaMUTFDEb79ZEjGOd8J7I8ak4Bua8rjBe/6tXr7br+pN+BHRt0u+a+CPi84CHLP0IkMqFhyx9CZT2d2H6kin9kZECiIcsPQkggOr/g/S8NoyK9Gb8XsURRCYCIpAbBLbKjWlqlqkmwD9beBciBjqvAHI/Ej7tvPoQRfHKQ5BKxBAj6d/ZoEGDEipug+ciIih2+OGHB4Ka69dfIsyR6xIvRwQ5jOPdnXYERYo3RTIK8DjBDo9RcoMWt5HOIJI5sZL9ruBRpLZFbcd71O+vqPb+fv81cNZZZ/m7Cq2TmxaDOXlFneFd7AxBFYEy1LhmeN/OnDnTjB8/PmHxM7Tf0nqOeLvrrrvaR7TXXWmNT+cVAREQAREQAREQAREQAREQAREQgUwgIA/QTLhKaTZGvGjIb0neRYxcjIRPO09PhCnyOI4bN87elSYXKJ6R4bwUI00Nz0/ETyc8nnHGGbbaeaT20bbj0emsWbNmbjXsEm9KHr7hkeisTZs2bjXskurrrVu3tt6jNOBYKpwXp1FQKZJR7MqZC/dwz+NdIsQlYlzDX375xR5KISZfyAzXn+9V6hd2OvDAA4PmVLzHSxdBG09X8sLCHnPLoHGCK/SDx29pGiGITuhlPITpyNKLAOFTXCd3wyS9Rpfbo+Ezz33uEbXAQ5ZeBLg5yU1Tfbal13VhNHz3uDB4bsa5m9vpN9LcHBHOB7x/eO+k6v+e3CRZPLP2vdpL+3/J4plhZvfKbxP+f+Pa6P2T2ddSoxeBeAno10C8xNTePPHEE4H4Sd5PQr39f4z5RxlPSITRvn372hyTDzzwgA3ljVRgx8f60EMPGRdyznb66Nevn98krnXCG5xVr17drca8XJxfWMeZX0ndbQtd+m188TS0XSqe849vNM9A/we/+yGT6HkTFUCpGu9ECMKA8LCN1XwBdKeddrLV3ceOHWt/lOFdOzE/PywPCnCRHxbxmbyfFKlK1mDLOUvT+AFKgSuM9xVhOrL0IoCnO6/v0n6tpBeV9BgNN+tcaCifCbHkK06PkefOKFauXGl/gPo363Jn9uk9U/53ctEzFFf0/59I75HnxugQcHj/EH0lgS39rjk58hGp+X2k/w/S7/pQ/4D/3/hs83/Dpt9INSIREIFUE5AAmmqiOdCfC2cmPyFh8JG+OPBMPPPMM20oMuLbU089ZQWsSIgQEcgnivcnRr8XX3yxOe644yIdEtN2vuCc7bLLLm415qULY+cO4c4771zkcf45+AeoOI0f9MWdY9SN35+X2xbL0hcxudsaTy6kn3/+ucApEE8RsQlvd9eFBogceIXyQCikSNKAAQN0V7cAPT0RAREQAREQAREQAREQAREQAREQgdwkIAE0N697wrMmEf5vv/1mj8cjsEaNGlH7IjwZsQr7/vvvI7YlRP7qq6+2+TZpxN3s4cOHm7Zt20Y8JtYdvmdJIkUwOB7Rjrvt3DEsyovI8WF83JkPZy5varh9bONc6WaRhO6ixul7JhCq3qlTp6IOCfaH8zhxaQqobE9hJyrZk2/VMcVrksJZVL2/+eab5RkR0NSKCIiACIiACIiACIiACIiACIiACOQmAQmguXndE561L+7F4g1Zq1at4Fx+KHqwMX8FD80LL7zQfPfdd3Yz/SJcUXQnFeaHbq9atSpql4hoiI++8MbxTrxdtmyZadCgQdQ+aOMsUsiyCwl37UKXCMLZYqFpD/DOTIW5KvSkR+B1+eGHH5rXX3/dvPvuu7b7Tz75xG4jJF4mAiIgAiIgAiIgAiIgAiIgAiIgAiKQuwRUBT53r31CMyes3RUrwMOuKE9FP4TZVUf3T0zoMmHuTvysV6+eue+++1Im1zq6XAAAQABJREFUfnIuv0iQH47tj8Ot41VIYZ0ePXpYL0K2+wKeE0Jd+3BLNxf2+efmuRNWXWEbtoUzVzQo3L5M20buI+cJC5uixF8Sx+PVSf5Ul3+MObOdwkdz584thAAvXa7bjTfeaM4555xgP/3IREAEREAEREAEREAEREAEREAEREAEcpuABNDcvv5xzx4BD5ESIxz8iy++iNrH+++/H+zfY489gnW3QgGlhQsX2qd77723DZdPpFCR6y/ckn6dzZgxw1b9c89Dl4RUk6+Uwj1OvGzcuHHQbMqUKUFV1GCjt4LA995779kt22+/vaFIlG8ufB6v1/Xr1/u7gnUEQp9bsKMYVlxYe7IFkooammOI8Dtt2rSozblGl156qendu7e54YYbgrZ9+vSxOWUvv/xyE81DlrQLzigQIBMBERABERABERABERABERABERABEchtAhJAc/v6JzT7o48+OjiOPJ2RvBV/+OEHQ0V3jMI0eOj59tFHH9miNWwj7P2mm24yfr5Ov20y63Xr1jVdunSxXRAC/+ijj4btjnlQ4R4jbyVVxTFCqJs2bWrX8QAlv2Q427Rpk6FCubOOHTuacuXKuad26YvALjdqgQb5Twj/X7RoUejmYnmOSIsxdt/bMtUnwyvTia28JnhthDO8PidPnhzswhPXWYcOHewqXsd+G7ffLX3x2Oft9mspAiIgAiIgAiIgAiIgAiIgAiIgAiKQWwSUAzS3rndKZksl7tmzZ9uCRQiKgwcPNqeddppp1qyZDRfHe3LWrFnmgQceMC7UGwHMD4GnGviYMWOC8eAZifAXiyFM+sJYLMecffbZdkx4rTIuQuF79uxp6tevb0OrCZW+6667rFcr/V1yySUFiueQo5SK9nhK3nvvvVag5PiGDRvaYxYsWGCPd5XJKQ41cODAQkOjor3LUfnyyy9bgRSBlTD7efPmmXfeecd6fyIIr1mzptDxqd5AeDqGqDhkyBDTpk0bG67ui9ypOCeceN1Mnz7d8urfv7/hcfDBBxtyrFKcirlPmDAhKLKF4NmkSZPg9F27djVPP/20PR4Rm7yfvXr1suwQV3ktzpw50+CliyHuck6ZCIiACIiACIiACIiACIiACIiACIhAbhOQAJrb1z+h2ZcpU8ZcddVVZtiwYWb+/Plm+fLl5tZbb7V9kR8UcdMZbU844YRCgiUFapxYSFvW/efu+HDLKlWqhNscdVvVqlXNqFGjzMiRI62wiPjIY+utty6UxxRRrXPnzgX6Q8AbMWKEGT16tA1dp9gOj3DH43FK6LbLe+l3RHg2VcydlyLh3jx8QwzlPCeeeKK/uVjWEV/xxMU+/vhj+6BwU6oFUPofMGCAfW0899xzVhi/++67DQ/SKoTmBSV1wDXXXMNhgSGUXnvttebKK6+0Fd+ff/55w4PXGAKon4+2Zs2a9npz3TPZ8Mp17wtE8UhFtTJ5jhq7CIiACIiACIiACIiACIiACIiACBQ3AQmgxU04S/tHWMJjEo+7Bx980LgK7774SRX3iy66yLj8jz6KSCHQfptUryOqETpNmDpemHiD+qIZwuWgQYPMgQceGPbUziOR48kVGnp8tWrVDF6KJ598cgHvUb8zxDpC/R9//HEzderUAnlA8YJt3ry5IcdlcaQC8Mfh1hGn8TTlOv766692s8tPWqlSJdcsJUs8Mi+77DIDR8L/Fy9ebD1qffGzVq1a5vTTTzedOnUKCkb5J2/btq31Ep00aZL1QmZfXl5ecB0Rx/FE5nWX6vH74yipdfLEvv322/Z0jRo1Mg0aNCipU+s8IpBWBJyHNzdqli1bZovxkae5ffv25sgjj7Qe32k1YA1GBERABERABERABERABEQgrQiUyRcP8tJqRBpMRhJAPEPQQkzDg7FOnTpp/YOUlz05PylaVKFCBRueH4+3IMfzI5zj8QIl5B0B1eW5jPUi4j1LESiOR9yK9/hYzxNLOwoGkQuUIlQuN2gsxyXaBu9Gcn6SjgDPRsTPXXbZJWYGGzZssN7HpFzgGpDvM55rmOi4S/I42LicswigCOyy9CLADQNE/Gx77aULZT4j8fzmRpt/w8ofHzc7SOHBjQ/yNzsjtQafExhtXBE6t1/L0ifA9w7XrKRu+pX+jDNnBNzYdrnB+W4mWkOWPgT4POT9Qyoj/3MvfUaY2yPhu4vfCvxfn+rirrlNNjWzx4mF/9+4NqX52ys1s1EvIiAC8RDQfzPx0FLbiAT4B8zlk4zYKI124ImJ95Cr9B7v0DgewY5HMobwySMdDA/WkjQKRCFaJlqoaMcddzQ8Ej2+JOeqc4mACMRP4L333rMpVPiRH83wlB46dKiZNm2aTSlCugyZCIiACIiACIiACIiACIiACPgEtvKfaF0EREAEREAEREAESpsAhek6duxoPZxiHQu5pdu1a2dWr14d6yFqJwIiIAIiIAIiIAIiIAIikCMEJIDmyIXWNEVABERABEQgEwgQlnb88cebP//8M+7hkl+aPMwyERABERABERABERABERABEfAJSAD1aWhdBERABERABESgVAmMGjXK5lhOdBCvv/66LeyW6PE6TgREQAREQAREQAREQAREIPsISADNvmuqGYmACIiACIhARhKg6Mo999yT9Nhvv/32pPtQByIgAiIgAiIgAiIgAiIgAtlDQAJo9lxLzUQEREAEREAEMprAm2++aajenqzNnj3b/Prrr8l2o+NFQAREQAREQAREQAREQASyhICqwGfJhdQ0REAERKAkCUydOtU888wzJXnKtDzXX3/9ZfLy8ky5cuXScnyZNqhvvvkmJUP+559/TO/evc2OO+5o+9t2223NNtvoX56UwE1hJ+R53XrrrQ3XR5ZeBPhs+/fff+2g+Hzbaiv5TKTTFeJ7h/dP2bJl7Xso0bFddNFFpm3btokeruNEQAREQAREIKMI6NdARl0uDVYEREAE0oPA/PnzzfTp09NjMBqFCIQhgBeoTAREQAREIDKBHj16RN6pPSIgAiIgAiKQZQR0OzfLLqimIwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi8D8CZfJDKPL+91RrIiACIiAC6UKAgjDLly+3w6lQoYKpWrVqugzNrFy50qxatSptxlNaA/ntt99smOhOO+1UWkPIqvM+9dRTZsSIESmZ0xNPPGGqVatm+9phhx3M9ttvn5J+1UnqCKxZs8amj+D6yNKLwPr16w1h8Bifb0ohkV7Xh/QEa9euNRUrVkwqBctuu+1m+0iv2WX+aPjfjZ/YpI6oXr165k8oy2bwxx9/mHXr1tlro/QeWXZxNR0RKIKABNAiAGm3CIiACJQWAX58IhBgiDeVK1curaHovBEI8A80+SbTSZyOMNSM2Pz999+bBg0aJD3W2rVrm6+++iooqFSpUiVTvnz5pPtVB6klwI2U7bbbTgJMarGmpDfENW7CYbvssosE0JRQTV0nCKC8fxCneQ/J0ouABND0uh6ho5EAGkpEz0UgdwgoB2juXOusmCkFMsjr9vXXXxtXLGP33Xc3TZo0MaeccoqJ5kWyZcsWc/vttxuWsdhhhx1mWrRoEUtTtREBERABEUgBgfr165uWLVuauXPnJtXbSSedZMqUKZNUHzpYBERABERABERABERABEQgewhIAM2ea5n1M5k2bZoZP358UJXUTRgPrM8++8zMnDnTXHnlleaggw5yuwosf/rpJ/Pss88W2BbtCcKqBNBohLRPBERABFJPYNSoUaZjx44Jd0zl9yFDhiR8vA4UAREQAREQAREQAREQARHIPgIqgpR91zTrZkQOnZEjR5o77rjDip/koWrdurW55JJLzLnnnmsaN25s54wQOnz4cLNkyZKwDL799tuw27VRBERABEQgfQgcfvjh5pxzzkl4QPfcc48N2U24Ax0oAiIgAiIgAiIgAiIgAiKQdQTkAZp1lzT7JvTiiy+aV155xU6MZO0IoeSjckao47333msee+wxQ06Xm2++2dx1111ud7BctGhRsD569GjTsGHD4Hm4FYrOyERABERABEqewLhx48yyZcvMjBkz4jo53qO9e/eO6xg1FgEREAEREAEREAEREAERyH4C8gDN/muc0TOkCAzePFi5cuVsDk9f/GQ71fsGDBhgEEex+fPnm19//dWu+38WLlxon2699dZm//33NzvvvHPUh5LK+/S0LgIiIAIlR2Dbbbc1Tz/9tLn22mtjqnDM98KTTz5p06CU3Ch1JhEQAREQAREQAREQAREQgUwhIAE0U65Ujo5zzpw5Zv369Xb2J554oqlevXpYEoiaXbp0MbvuuqvZZ599DPk+Q80JoHXr1o3pB3Xo8XouAiIgAiJQcgS4uUVaEwreDR482JCXOdSaNWtmbrzxRvPdd9+Znj17hu7WcxEQAREQAREQAREQAREQARGwBBQCrxdCWhN46623gvEdddRRwXq4ldNOO83wCGerV6825AjF9tprr3BNkt42adIks3LlStvPeeedZ8qXLx+2T0L48WzFBg4caMKF2pP3lBBQ2iHqnnzyyYX6+uKLL8zLL79sFi9ebH788Uezww47mAYNGthH165dTdWqVQsd4zaQMmDDhg1m3333tcLxqlWrzNtvv23mzZtn6JdzNm/e3OZaRVB2tnTpUtvuk08+saJEtWrVbCqBPn36mFq1arlmYZf//POPeeGFF8xXX31lSEewfPlyU6dOHXv8AQccYNq1axf2ODZ+/vnnQRqE/v37m5122sl89NFH5sMPPzQLFiyweV/pa8899zSdOnUye++9d8S+tEMERCCzCCB8jh071j74LCc0vmzZsvYzh4JHMhEQAREQAREQAREQAREQAREoioAE0KIIaX+pEsDzB0Noq127dsJjcd6fdOCLY5s2bbLh8jVq1DB4kSZjhN0/99xztgsq0bdt27ZQd99//72hmr2zSO0o2OTanX766a65Xa5du9bcfffdgSDodnJ+PF9nzZplQ0HxmEIMDGevvvqqQfT8+++/LY+LLrrIrFmzJmjK+meffWYmT55sRQdSBiA0Xnrppeb3338P2uGdC9s333zTFqqiOFU4Q6S97rrrbFt/P+kKeDzzzDPmsMMOs/2HEzQobOXYIgaz/sADD/hdWYEboZS+EJZ79epVYL+eiIAIZD4BbuxEu7mT+TPUDERABERABERABERABERABIqDgATQ4qCqPlNCYPPmzQaPQ2yPPfawyy1btlgh7tNPP7UCHQImHp14MiKgETIZznwBlFyheGs+//zz1guR9ngT1a9f3wqG3bt3N2XKlAnXTdRtBx98sJk+fbptM3fu3LACKB6LvuFJGU4off/994NmhxxySLD+77//miuuuMJ6UbIR71HOC4ONGzdaNpwb786RI0daT0u8USPZDz/8YM4//3zz22+/2XyorVq1MgiQeFcSUsr5yMF3wQUXmFtuucUWmcJbE09LvF1ph2ct12rMmDFmypQplqV/PrxKEU6d1ytj5TyIzgjCH3zwgRVuEVHxPp04caL1ZvX78NcRf2fPnm0Fa/qpV6+eHQssEW4Z85133mnDZdkvEwEREAEREAEREAEREAEREAEREAERyG0CEkBz+/qn9ewJkSYUHKtUqZIhhBpR74033igwbjwTER5nzpxprrrqKustWqBB/hO/AjxtEE59Q5z7+uuv7YNQcNog0MVjeEkiSOIhiQgZzkIF0I8//jhcM+MEUAp7+CH7CIyEkGNsx6syNPQcD1By4jFHioJ07NixQB/+CZkzhpfpWWedFeyC9eWXX25DzPE4HTFihBUlCUNt0aJF0I5wVI5zYanMG0HWGf0gjDrxE89Mcrn6QjXi6W233WZeeukls2LFCvPwww9bwdX1EbpE/ET0ZEwsnXGuSy65xCCEYhRQyXQBFFHbvUbwgCbfoUwEREAEREAEREAEREAEREAEREAERCA+AuHd5eLrQ61FoFgI+CIlHpp4PiJ+EqpOrsv27dvbpQtdR/jq169fgVBuNzBCyp3RL/kijz/+eHP11VcbPCTxHnWGdyniH+Hh8dg222xjc2ZyDJ6rv/zyS4HDEQHpGyPHJoaXpctNajfk/8GL0QmTiInOG5UwcsRBjAr1eGSGip/s69Chgxk0aBCrBo9ZxEUnJNuNIX/atGljyKvpG3M55ZRT/E2mb9++BcRPdhKKynZnP//8s1u1yxkzZtgcpTwhhyvh6774yfZy5cqZoUOHmsaNG/PUitnkNI1k8KC9L37SljEjkLPE8CyNNm/bKM3/8FpF8OYRyjbNh67hiYAIiIAIiIAIiIAIiIAIiIAIiEDaEJAAmjaXQgMJJeALoHh3ImgRCo8ISJg0YhfLhx56KBDDyElJ8SDf6McXj/BAxJPy4osvtiHvJ510kvUmHD9+vKlSpYo9lNDwRx55xO8mpnXf+zHU25Ncl3g7YqeeemrQHyHivlH53plfGIhwc8K7Maodu7G6tv6yc+fOQcVkxDNyg0ay3r17ByKr3ya04vJxxx3n7w7WSSngDK9d3/DCdOZ7mLpt/pLrgjHH1157zd9VYJ3iTH4eV38n4ft169a1m+jHz1fqt9O6CIiACIiACIiACIiACIiACIiACIhA7hCQAJo71zrjZup7YCJm4W2ISBnq+UfuTkRPqqBj5JJ0npY8xyOQ0O0hQ4bY0HZyXjqvSvY7a9q0qQ2hds8pAOSLsG57tCVFgJxHKoKtb04QrVmzpg1Ld+1cyLZr+95779lV5kNYvTMKATnDazOa0bdfkMg/NvQ4P8Te31exYsXgKezx1AxneKM6C71mzguWORdVuCS02rzrM3RZVDEsf9z+eEL70XMREAEREAEREAEREAEREAEREAEREIHcIKAcoLlxnTNyluXLly8w7nPPPdeEbnMNKleubL0q7733XruJCuZOPCR83s9b6Y4Jt8TjEi9TiiYhui7ODzv3hblwx/jbnGhJ3kaETfJSupBsJ4AyFsREvBgp+uNyPNIPgh2enhgipzuW54zFGYJiUea3iSSAbrvttjacPlxfvkgcTbwMDWl3fZHPk/lj5Agl5UCs5opfhWtfVG5W5uQs00Pg3Ty0FAEREAEREAEREAEREAEREAEREAERSJyAPEATZ6cji5lAqNiJh2Y022+//YLdvlgYbIxxpWHDhkFLqpTHay4MnvDrL7/80h5Onk+Xh9SJsVRTxwhPX7VqlV0nHP6PP/6w6371d9eOJd6dO++8s20T7Q8FlJyFhqa77b73ptsWbhlJ5AzX1m3zRUyEXUTQoh7uWD9lgdvmlhSakomACIiACIiACIiACIiACIiACIiACIhArATkARorKbUrcQK+gIfoV61atahjcIWFaOSLb1EPCrOzevXqwVZyisZrbdu2NXfccYc9DK/PJk2aBF6dbPQF0EmTJtl2eIt26tQpqP6O12poBXNCuxEQ8UxFJA0ViG1H3p/ffvsteObSAwQb/ruSiLAZ2kek57642qhRIzu/SG1Dt/uer6H79FwEREAEREAEREAEREAEREAEREAEREAE4iEgATQeWmpbogQqVapkPR2pio7oh1egL3KGDmbDhg3BJorhOCMMGw9MHoiq9BvNfG9Jv8BPtGP8fYSeU6WeCu9z5841Z555ZiCAsn2nnXayzffdd18bfv7nn3/aMHhfAEUkDRU4mbvzSF22bJk9h3/e0HXaOCNFQElbKLvu3buX9BB0PhEQAREQAREQAREQAREQAREQAREQAREwCoHXiyCtCfj5NxcsWBB1rL7Xp5//csKECTb/ZN++fc306dOj9sFOP3y+Tp06RbYP1wAvUIwK7HhiukrvzvuTfXg5urB+8oBSed6JlqHh77T3BUUnhLI9kiHAOiuqcJBrl8olQq/zPGUsLh9opHMgBFM4inylmzdvjtRM20VABERABERABERABERABERABERABEQgLgISQOPCpcYlTaBjx47BKd944w0TrajNCy+8ELT1BcQDDzww2D579uyofRCK/vXXX9v2CI6+6Bh0EsOKOz/jffHFF42rhu4LoHTj8oCuXLnSPPnkk7Znig+5PKL+qRo3bhw8nTJlitmyZUvwPHQFwdFVk99+++2D84S2K+7nbsx//fWXmTZtWtTTzZgxw1x66aWmd+/e5oYbbojaVjtFQAREQAREQAREQAREQAREQAREQAREIFYCEkBjJaV2pUIAT0qXk3POnDnmvvvuCzsOxD7ETYxcob7oSXEk8mdiixYtMk888YRdD/3z66+/mjFjxgSbqTpP7tFEbK+99goKFSFWYvTlKtO7Pp0AynOEUozQeBcmbzf89w+iqvMYxQP0scce83cH65s2bTJjx44NniMiU3W+NOycc84xLs/oQw89ZL1cw40Dr8/JkycHu3r06BGsa0UEREAEREAEREAEREAEREAEREAEREAEkiEgATQZejq22Akg3F1++eWBiDZ16lRz22232ZByQqpXrFhhhbOhQ4cGYxkyZEgB4ZI+rr766mD/3XffbYVOvDLx0ET4xLu0T58+5scff7TtWrdubZwXZ3BgHCt4cboweFdIiXD+0Lye5AR1+TmdR2e081544YUBi3vvvdeMGDHCVprHw5LzIAT379/ffPrpp3a0NWrUMAMHDoxj5Klt2rBhQ5t+gF4p3MTYEG6pfA978rbiuTto0CCbKoB2HTp0sIWjWM91I00Crw8eFSpUyHUcmr8IiIAIiIAIiIAIiIAIiIAIiIAIJERARZASwqaDSpJAy5Ytze23326GDx9uxcpnn33W8EAc8vNKIjqef/75hvah1qZNGzNgwABDPlCExmeeecY+qLaOeOgbxXroJ1lDyHzuueeCbkLD39nBmPECff3114N20QRQBEVEz9GjR1vBk+N44F1KoSjf6tata0PJXR5Of19JrsP977//tixgjQDNI/T6MSZYXHPNNSU5vLQ+V5UqVUzXrl3tGEllIBMBERABERABERABERABERABERABEYifgDxA42emI0qBQPPmzQ0h1OTGdEKQL34S5k74eq9evSKO7tRTTzUPPvigadasWdDGiZ8IiHhjXnXVVQYvS8S5ZI0xu7HSVzgBlO1+GDyiZbRK97THQ5Jw8UMPPTTo3xc/SQHQr18/K/YmWsSJ86TKYHDZZZfZ61O/fv3Ag9W/frVq1TJXXHGFufXWWw2itEwEREAEREAEREAEREAEREAEREAEREAEUkWgTH4Yal6qOlM/IlASBHjJEkJNPk9CyhH5ENDisd9//91WG//5558NYeJ77LFHqeXJjGfcoW1hQeV4ih4h4jIXRFSXdzO0fTo8p8I7OT+XLl1qQ7u5drvssktaj7m0uCHQr1mzxp4eIdmlSyit8ei8hQmsW7fOeqJXrVq18E5tKVUCGzdutGk2GESlSpUKpSAp1cHp5JYABQC32267IE+3sKQPgbVr1xq+rzG+o1NxYzh9Zpf5I+HGN+8fcsbzHpKlF4Hly5fbVE/8P+5qGaTXCHN7NKTl4v83rk06/2bK7auk2YtA8RBI3s2teMalXkUgIgHCxhE9k/FuJJ9io0aN7CPiiTJgBywQEOMVgEtzauRkRXDmIRMBERABERABERABERABERABERABERCB4iagEPjiJqz+RUAEREAEREAEREAEREAEREAEREAEREAEREAESo2ABNBSQ68Ti4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIFDcBCaDFTVj9i4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIlBoBCaClhl4nFgEREAEREAEREAEREAEREAEREAEREAEREAERKG4CEkCLm7D6FwEREAEREAEREAEREAEREAEREAEREAEREAERKDUCEkBLDb1OLAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUNwEJIAWN2H1LwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUGoEtim1M+vEIiACIiACUQmsXbvWvPTSS7ZNgwYNTPv27aO2104REAEREAEREAEREAEREAEREAEREIHCBCSAFmaiLSIgAiKQFgT++ecfs2bNGjuWatWqpcWYNAgREAEREAEREAEREAEREAEREAERyDQCCoHPtCum8YqACIiACIiACIiACIiACIiACIiACIiACIiACMRMQAJozKjUUAREQAREQAREQAREQAREQAREQAREQAREQAREINMISADNtCum8YqACIiACIiACIiACIiACIiACIiACIiACIiACMRMQAJozKjUUAREQAREQAREQAREQAREQAREQAREQAREQAREINMISADNtCum8YqACIiACIiACIiACIiACIiACIiACIiACIiACMRMQAJozKjUUAREQAREQAREQAREQAREQAREQAREQAREQAREINMISADNtCum8YqACIiACIiACIiACIiACIiACIiACIiACIiACMRMQAJozKjUUAREQAREQAREQAREQAREQAREQAREQAREQAREINMISADNtCum8YqACIiACIiACIiACIiACIiACIiACIiACIiACMRMYJuYW6qhCIiACIhAiRLYfvvtzR577GHPWbNmzRI9t04mAiIgAiIgAiIgAiIgAiIgAiIgAtlCQAJotlxJzUMERCDrCOy4446mVatWdl6IoTIREAEREAEREAEREAEREAEREAEREIH4CSgEPn5mOkIEREAEREAEREAEREAEREAEREAEREAEREAERCBDCEgAzZALpWGKgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjET0ACaPzMdIQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECGEJAAmiEXSsMUAREQAREQAREQAREQAREQAREQAREQAREQARGIn4AE0PiZ6QgREAEREAEREAEREAEREAEREAEREAEREAEREIEMISABNEMulIYpAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQPwEJoPEz0xEiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIZQkACaIZcKA1TBERABERABERABERABERABERABERABERABEQgfgISQONnpiNEQAREQAREQAREQAREQAREQAREQAREQAREQAQyhMA2GTJODVMEREAEcpLAli1b7Lzz8vJycv6atAiIgAiIgAiIgAiIgAiIgAiIgAgkS0ACaLIEdbwIiIAIFBOBlStXmscee8z23qhRI9O1a9diOpO6FQEREAEREAEREAEREAEREAEREIHsJaAQ+Oy9tpqZCIiACIiACIiACIiACIiACIiACIiACIiACOQ8AQmgOf8SEAAREAEREAEREAEREAEREAEREAEREAEREAERyF4CEkCz99pqZiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQ8wQkgOb8S0AAREAEREAEREAEREAEREAEREAEREAEREAERCB7CagIUvZeW81MBERABERABERABERABERABERABERABEQgQwmsW7fOzJkzx2y11Vamdu3aZq+99jLbbCMpL5HLKQ/QRKjpGBEQAREQAREQAREQAREQAREQAREQAREoYQJNmjQxZcqUCR5Dhw6NawTHHXdccCz9zJ07N67j42n8/vvvF2qOoOfGP2zYsEL702lD/fr17VhhVtL2ww8/mGbNmpmdd97ZdOnSxXTq1Mnsu+++ZuPGjSU9lKw5nwTQrLmUmogIiIAIiIAIiIAIiIAIiIAIiIAIiEAuEXjqqadini7i48svvxxz+0Qbrlixwpx22mnm8MMPT7SLnD5u06ZNplu3bubTTz81W7ZsCVjgAVq5cuXguVbiIyABND5eai0CIiACIiACIiACIiACIiACIiACIiACaUFg0aJFZt68eTGN5emnnzZ//fVXTG2TaTR8+HAzZcoUk5eXl0w3OXssXrmff/65nX/nzp3N7Nmzrefnxx9/nLNMUjFxCaCpoKg+REAEREAEREAEREAEREAEREAEREAERKCECNSoUcNst9129mxPPvlkTGd9/PHHbbuKFSvG1F6NSofAkiVLghMjJh988MGmQoUKpnr16sF2rcRPQAJo/Mx0hAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiUGoEdd9zRHHXUUfb8sYTBr1y50rz55pu2/fHHH19q49aJiyawevXqoFHNmjWDda0kR0Clo5Ljp6NFQAREoNgIcGe2bdu2tv9ddtml2M6jjkVABERABERABERABERABDKPwIknnmgIa//uu+/MJ598Ypo3bx5xEtOmTTP//vuvadq0qdlnn30itvN3EMK+cOFC89lnn5kvvvjC1KpVy+y///5mv/32M9tvv73f1K7/8ssvZunSpQaxFeN4V2QJj9U6derY7aF//vjjDxvG/+GHH5q///7bHHTQQaZFixamXLlyoU0LPWdOhIZ/+eWX5vvvv7fnoFBUpDGGdrB+/XrzwgsvGFIJUHAIb0uOpep6LAYf5sg1wCMXtjzq1atnCyjF0odr88033xjG43uAEgq/fPly24TrG1oBftmyZfbaf/XVV2bz5s2GufPYfffdXbeFlosXL7bXqFKlSraqPNfrueees9tg36FDh0LHZMMGCaDZcBU1BxEQgawkwBcoX5xYuH8wsnLSmpQIiIAIiIAIiIAIiIAIiEBMBLp27WpDo3///XdDGHw0AdSFv5900kkx9Y0geOqpp5oPPvigUPtq1aqZBx980HB+3x5++GFz9dVXB5vIN9qqVSv7/MILLzS33357sM+tkN8SIRchz7eyZcuae+65x/Tr18/fXGAdj9bBgwebBQsWFNjOk6pVq5q7777b9OzZs9A+t+GGG24wN998s9mwYYPbZJcIiIii0WzNmjV2bDNnzgyb65QCUBMnTjS77rprtG4K7LvgggvMa6+9VmDbMcccEzzHMxSRFvvtt9/MddddZ8aNG2dF46DRf1e6d+9u+YVzpLnpppvMfffdZ4488khz/fXXm44dOxZgQL/XXHNNaJcZ/zw2STvjp6kJiIAIiIAIiIAIiIAIiIAIiIAIiIAIiED2EChfvnwgQkYLg8cr891337UTj0UAxasUT0/EzzJlypiWLVuavn37WqGMKDU8BhHmhgwZUgAm4doHHHCAcaIbx/Kcx2677VagLU84z2GHHWbFT6qbd+rUyXpfsg/x9KyzzjJTp07laSFD3ERkRPzceuutTbNmzWzleTw4cR5BLOzVq5cdd6GD8zecffbZVqxF/Nxhhx3MEUccYYVYvFTxukS4/fXXX8Mdav788087f7wm8XKl7RlnnGFOOOEEg1cl9sYbb1hPTLwtY7W99tqrECvEWMfQeX8yZuZ72223WfETQfroo4+2861bt6493fTp082+++4beOCGGwOet6ecckoB8ZN2XIestPyLJRMBERABEUhDAvkhDHn5YST2kf/lm4Yj1JC4LqtWrRKINCSQ/49h8P7J94pIwxFqSCtWrMjLD/MSiDQkkO/VErx/8kMR03CEuT2kf/75x16f/B+uuQ0iTWef78Vmr09+yGqajjC3h7Vp0yZ7ffLDpjMWRH54NqXV8/bYYw87h3wR0T5n20cffRR2XvlCmW3Tpk0buz/f6zE4Jl/kLHDM2rVr8/K9DO3+fNEyb9asWQX25wuLefliYXB8vrBaYD9PzjnnHLs/P4S90D7+f2as7pFf3CfvxRdfzOOzzRnPt912W9umSpUqeaHX69tvv83LF3/t/vxQ77w5c+a4Q+0yX3TMyxdtg3M89thjBfbTvzv/gQcemJcvEAf7t2zZknfVVVcF+2l37LHHBvtZyfemDfbnC7EF9vG96fMdNmxYgf2xPBk9enTQ/08//VTokP79+wf7871G8/jd6Nsdd9yRl+9Ba9vki6B5od/lAwYMCI5nfr179857++238/K9XvPyPXj9rrJqXR6g+VdbJgIiIAIiIAIiIAIiIAIiIAIiIAIiIAKZRqBLly6GgkhYJC9QF/5+8sknFzm9fMHOEN5NDkyOa9++fYFjCMHOFxBt7kh2DBo0yOSLhgXaxPqEcxC6zxzw4nTG83yRzz7NF2Rt/lG3j2WfPn1Mvphtj3nppZeCMHvXhvyXeGC6qukXX3xxAS/HUaNG2aakHCOEvXbt2u5Q6/E6cuRIG/4fbAxZIWwfyxdnrSepvxsvTc6HNyeemflirb876XVC5B944AHbD9eTEHjSBfjGNSG8H8NDduzYsf7uAut4zE6ePNm0a9fOFtUiJD5bTQJotl5ZzUsEREAEREAEREAEREAEREAEREAERCCrCSDiHXfccXaO4QRQivNQXAiBkZDwaEZBoQkTJtgmiJAUxAlniHyIbBjFl8Ll4Ax3XOi2fC/WoJJ96D7/3K6oEm3WrVtn3n//fducHKWNGjUKPdQ+J6w935PTrpNfFEEUQ6ydN2+eXSes34mkdoP3Z+jQoRGLGLnK7IizFJcKNfhQNCo/2sXke5+G7k7quctNyvUkV2ckO//88wNhd8qUKZGamfPOOy/mgk8RO8mQHRJAM+RCaZgiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEEqAIkIYVdCpiO7bE088YZ8eeuihEcU+157q41QSx6gWTw7MSI8999zTHWYrxQdP4ljBSzKSOZGR/RT8cUa1c2cU74lm5PV0RoV1DEGYolGYL7LaDd4fhFWq3oczxGFn5FRt27atufHGG60YnB8zbnf5Hq2ubSqWbv7k+mzYsGHELhHG8e7EqFTvxhV6ACJ0rpgE0Fy50pqnCIiACIiACIiACIiACIiACIiACIhA1hGgmjdFhDBCyn2LJ/zdD9cmTJwQ70gPX1xEYEvEfBE19HjC45354p0TANnnCv64dqFL9lOICXMC6GeffRY0K6pCe7jCTRzcvHlz6ynrihLhkYrHaIsWLaxoimfpyy+/bPLzmgbnStWKm39Rc+d89erVs6clXUB+LtGwQ4gmooY9IIM3/u8VlcGT0NBFQAREQAREQAREQAREQAREQAREQAREIBcJkAPy+OOPt1P3w+AJw54/f77NEUmF8qKMavGJWCRxrai+qNYer+UXiQ0OcdXmgw0hK3hBunOQ1xTDA9QZ4m40q1GjRsTd5CjNLxBlevbsaTiPs/wCaGbixIk2r+khhxxi/PG6NokuEYIJ58eKmjtt/Pm5+bPdGQKuq1rvtmXzUgJoNl9dzU0EREAEREAEREAEREAEREAEREAERCDrCbgw+B9++MHkV4O383Xh7507dw48RKOB8D0iKYxD6HksjzFjxkTrNqX7KHDk7Oeff3arYZeE7+P9iFWtWtUuKeLkrChxMpxo6I5lSeg7HrfkAsXj86KLLiqQkzS/Or3JrzJv8quw+4clvI43a506dezxRc2dRr6g7eaf8Mmz4EAJoFlwETUFERABERABERABERABERABERABERCB3CVw+OGHGyfuOS9QJ4DGUv0dcn4+SELGqS4fy6NcuXIlBt4f4+LFi6Oe19/vih35Ye0//vhj1OOL2u8Oxsu0U6dOBiH4yy+/tAWQ9tlnH7sbkdUVbXLtk1m6+ftzi9Sfa4NwGovHaKR+smW7BNBsuZKahwiIQNYR+Ouvv2yIA2EO69evz7r5aUIiIAIiIAIiIAIiIAIiIAKpIUA4c/fu3W1n06dPN59++qkhp2eFChXMscceG9NJyCvpwrmpNk7F9EiGyEqhJCrQv/vuuwWaubybfu7OAg2SeELeUNd/URXWH3300eBMHTp0sOuEpZcvX96uh6vg7g5AyAwX2v/vv/8acny2bNnSnH766a55gSXi58033xxs++CDD4L1ZFdc4SjG9t5770XsbuXKlUHl+9atWwfXNeIBObBDAmgOXGRNUQREIDMJrFu3zn5pvfHGGzZ3T2bOQqMWAREQAREQAREQAREQAREoCQIuDJ48l9dee609JeKnE/yKGgOFhwYPHmybIaDec889YQ/ZuHGjGTZsmPn888/Nq6++avbee+8C7ZyIikMHbVNp5LXs06eP7fL11183r7zyStju8d6866677D68Hw877DC7DgvHiePffPPNsMczv3ACLtXd8az88MMPzZQpU6zQHK4D/9ho1ebDHRtt2wUXXGBzutLmiiuuiFhoacSIEUG1ezffaP3mwj4JoLlwlTVHERABERABERABERABERABERABERCBrCbQvn1740K9n3vuOTvXWMPfHZirr77a1K5d2z5FDL3qqqsCIW3z5s3mpZdeMkcddZT5+uuvbZuBAwcG+TVdHzvttJNbNQhxs2fPtqHhwcYkV2666aageE+3bt3MfffdZxgbhtcq+ThbtWpl/vjjD7tt/PjxxlVsZ8Odd95pmjVrZvchEJMqAM9OjMi7M844w+BFG8mcAIvIec455xhXmd21xyP2vPPOs08RX1MpgFK1/ZJLLrF9cx6EXb+wE/lIGd/dd99t2+y///52jG5subyUAJrLV19zFwEREAEREAEREAEREAEREAEREAERyAoCeCe6MHgmhBBJbsp4bIcddjCPP/64IVcmouCoUaNMxYoVrZcnhXQQPxE0MSrL33LLLYW6R4h1YeqjR4827dq1M1deeWWhdoluqFatmpk6daoVXv/8808r8DHGfffd1wqjXbp0MStWrDB4tDL+Xr16FTgVaQFmzpxpatWqZcXdk046ybJq1KiRrZxOASiKF1HkKJwhkCIyYoS3N27c2FBAqnnz5obK8YTZE6Jes2ZNex7GkUpDlO7Ro4ftkmuBKMpcWJIHdtKkSXYf40LILckcramcZ6r7Su1VSPXo1J8IiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEBMBPxwZ8TQsmXLxnSc3+jggw82CxYsMGeddZYVP/GqpCiSC2evV6+emTBhgvWcRHQNNQRPvC4R5ZyFekm67Ykujz76aFtsqGfPnjbEn3D7L774wo4RwRHh96233ooovOLlSvh+586d7RA2bNhgvVrxFD3mmGNsKjLC7SPZ/fffb1MEkDcVT1Cqss+bN88Kr5UrV7Z9fPTRR9YTNVIfiW5HwCUHKzlQuRYYdSOcJygiLJ68c+fONfXr10/0NFl3XJn8C5WXdbPShERABEQgCwgsXbrUfqkxFe5Gdu3aNQtmlV1TIE/rP//8UyjsJ7tmmZmz4R90/pHFKlWqFHPuq8ycbWaOmuT85AjDY0OWXgQIn3OhhITu+WGD6TXS3BwNHlm8f/Dscnn2cpNEes56+fLlVgxBgHGhyOk50twcFSHR/P/GtUm1V142E8WbEXGRzxwENbwdY+WHMLdp0yZ7THF5IiLQIv4Rls9nI5XS43n/IV5yLGIxnp/xfLZybn63LVmyxL628Lp0omRJvSYI258/f74N3999992tt66+uwvT36bwJm0RAREQAREQAREQAREQAREQAREQAREQAREQAWPD4QmJT8QIAy9uQ4xF9OSRiOEN6vKexns8565Tp459xHtsqtpzsx+vXVl0AgqBj85He0VABERABERABERABERABERABERABERABERABDKYgATQDL54GroIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEB0AhJAo/PRXhEQAREQAREQAREQAREQAREQAREQAREQAREQgQwmIAE0gy+ehi4CIiACIiACIiACIiACIiACIiACIiACIiACIhCdgATQ6Hy0VwREQAREQAREQAREQAREQAREQAREQAREQAREIIMJqAp8Bl88DV0ERCC7CVStWtWccMIJdpI77LBDdk9WsxMBERABERABERABERABERABERCBYiIgAbSYwKpbERABEUiWwFZbbWXKly9vuylXrlyy3el4ERABERABERABERABERABERABEchJAgqBz8nLrkmLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQG4QkACaG9dZsxQBERABERABERABERABERABERABERABERCBnCQgATQnL7smLQIiIAIiIAIiIAIiIAIiIAIiIAIikGkETjnlFFOmTBn7WLx4cczDp6YAxzVt2jTmY9Kt4eTJk4O5z5s3r8Dw+vbta/ftuOOOBbZv2LAhOGbo0KEF9qXzkxkzZgTjdtc73uW0adPsFDOVQaqvjwTQVBNVfyIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQRgTy8vLSaDTFN5RcmWfxEczenlNeBOmOO+4wb7zxRkCMIh4PP/yw2WmnnYJtRa1s3rzZnHrqqYals5tuusnss88+7mmpLC+88ELz/fffm6233to888wzxTKGE0880fzxxx+mZs2a5r777kvqHO+88455+eWXzY8//miWL19uKlWqZOrVq2eOOOII+2Ae2WT//vuvOf7444MpcY3SfY6Mec2aNaZatWrBuFnhPcR7Cevfv7859thj7Xron0jHh7ZLxfNly5bZ12VoX7GONfQ4PRcBERABERABERABERABERABERCB4iSA1+R2221nT7HNNimXwIpt6I0aNTI33HBD2P4//PBD8+yzz9p9aCAHHHBA2Hb77ruv3Z6pDMJOKomNKb/6v//+u/n1118LDOntt9823bp1K7At2pP333/fCnZ+m3/++cd/Wirr69evt3MrTlENdgigrvJzIhNdu3atufbaa82nn35a4PBVq1YZHnPnzjVvvfWWue6660y2VZYOfe0VAJBmTz7//HMzZswYc8wxx5ju3bsXGN1ff/0VvI/8GwF+o2jH++2SXef1OGnSJCumuw9Zv89Yxuq317oIiIAIiIAIiIAIiIAIiIAIiIAIlAQBQv/5TZtptueee5pIIfsPPPBAIIB27drV9OvXL+r0MpVB1EklsDPlAmi4MSC2xSOA+h6k4frTtsgEEIqvvvpqM3/+fNsIz8Kjjz7a7Lrrrlb8fO2118x3331nEJkvuugic/PNN5vQHBmRe9eeVBF49913zZVXXplwd8keH8+Jzz//fPPtt9+aihUrxnOY2oqACIiACIiACIiACIiACIiACIiACIhAWhAoVgEUN+M///zTeiLimRdLGPymTZvMf/7zn7SAk4mDIN2AEz+bN29ubrzxxgLepITYk07glVdese3uvfdec9lll2XiVDN6zCQhdoY7eqg1a9bMXH/99XZzw4YNQ3eboo4vdEASG3777Td7dLhxsqOosSZxah0qAiIgAiIgAiIgAiIgAiIgAiJQggTwlsQB5uuvvzbffPONDR/fY489DA/SEpLmMNSWLFlio3hxmtl7773t7pUrV1pth8jU3Xff3aBPEJId7vjQ/ubMmWM++ugjs3r1atOiRQvTtm1bU6VKldBmMT3fsmWL7YvGtWrVss5h7sBUjpuI4RdffNGmTUQLa9OmjZ0z60uXLjW//PKL2X777c1+++3nTl9iy2gM/EGQP3XhwoXms88+M1988YXltf/++9sxM/aijGOJOMbpjnnzeuFBKsZIekJRfaZyf7EKoAcddJB58803DbBnzZpVID9jpEnMnj3bEFLLm4I3zpdffhmpqbaHEODF+tJLL9mthLbjCRoaSk/Oi4svvti89957ZuPGjTasefDgwaZs2bIhvRXfU8Kon3zySXsCPFB322234jtZhvZco0YNwyMTLJPGmgk8NUYREAEREAEREAEREAEREAERKA0C1CG59NJLrVYQ7vytWrUyhF+73JKuDandqGHRvn17Q9QpIdlTpkxxu4PlwQcfbB5//HFTu3btYJu/guDap08fgwDqG/rQbbfdlpAISppGxo0RhTlq1Kig61SNm1ydaBu+oxInQXClNgkaCM5phLUzx5K2aAzcWBYtWmRr8XzwwQduU7AksvjBBx80hNuHM+qacM1nzpxpwhWhOvzww83EiRMLiM/h+inubcUqgPLiJlQXQZMweL9ATaSJufB37g7EojBH6icXtyMWk+MT4y7JLrvsEhYDoihehdyJ4dpQIKlOnTph2xbHRu6M/PTTT7brdMjtWhxzVJ8ikAoCfFGR6xWjMFrlypVT0a36EAEREAEREAEREAEREAEREIGAAKIVqfOcQxXCXevWra3giOciXn14YyKO4dmI52S4CF9+vxx33HG2H1LtIZT+/fff5pNPPrGOcehDBx54YOAlGQwgf4XfPYcddpgtEsz2vfbayxb3oagzRX9I4VdchbGTGfeAAQPM/fffb6dSoUIFyw3BEFYU0UYUbty4sd2frn+efvppc/rppxs44KnJNWLM6DZce7x5qV1y+eWXW6HXnwdR3+xzkdyIzTgzIgaj76H/sGzSpIl9HdStW9c/vETXi1UA5eIzebw6caGlOE80t2VCbYGLdezY0XopxkID4KjqCICLFy+2xWNwsW7QoIF1Oe7QoUMs3ZgffvjBeiZ+9dVXVkjkwjRt2tRWTMdlN1bD7RePV5ao6Ntuu611F8dlHMV85513jrWruNrxAp0xY4Z1Ny4qXyOpBjDupIQTVQiRd8ILLs9Ujs8GI+SfNyIfnHzAr1u3zr4ZFyxYYHhQ4Iq7Mrilk7eWaxfJqMDOdebN/PPPP9vXDB90vFZ47XTu3NlUr169wOEff/yx9Yp2AjA7X3/9dXvNWCdFAWI0Y3FfPnwJIGhjsR5PuAA3HTDmGe2LggJHfKBhfjqEO++806aw4AMLIxTi1ltvtet47Z500kl2PdJY7U7vTzLvU16LvCax/v372y9b5sgXIefnCxhuXLtOnToFYRfe6TNy1RdA+ceBLw2ZCIiACIiACIiACIiACIiACEBg2rRppmrVqjHBiOZ8xG9P9/vzvPPOM7fffnuB38JEj15wwQXWi491fleHq2nBbzSMtnhFunojeAgeeeSRVgBbtmyZeeyxx0zfvn2DcSPAnn322Vb8JGp17NixhnE4Q6M59thjiy1CONFxP//884H42bJlS3s9/AhXPEyHDBlif8czl3DekW6OpbUkXSXs+e3J2B955BEr2rrxcO1OPvlk69l7yy232OtASgJnaFBO/Lz77rvNueee63YZXnOOAechZeOIESOC/SW9UqwCKJPB1RUBlDB4qsFH8wJlP6ISL/h27drFJIASYn/XXXcFAo4DiGDCA2GUUHyU6mjCI4o9F9o31wcfKsOHD/d3hV1njrh5P/TQQ3YefiPumjA/+mIshxxyiL87ZesIzNFEZk6EEE1OBgzRNJxYincob2aMOwDZIoC++uqrVqhEjMPLmHQA5OPwDTET8ZC21113nfW88/ezjnDKhzqCu2+Iq44trv3077PjDtBzzz3nH2Jzsbq8rQj/CHkIpK4dYqoTQGM9HuHdHY+IH00ARcSlPeYLoOQv4cvNGd7Crk9EcSeARhqrO45lsu9TBE53bj58WSf0wjeuiXvPDxw40PTq1cvfrXUREAEREAEREAEREAEREAERyCoC/u+3ZCZG+DpG8WTWcQzyjSri48ePt3oHola4MGnXHge0cePGuad2iRYzffp06yzEBn7P+QIovxddn/zO9sVP2jdq1Mi88MILdonuUhyWyLjRCzCEXsYXKkajBxClSx2WdLVhw4ZZ4RnnODQM9DPfuHZoA3jzEr4/aNAg64hEewy9D0OHQkj1zaVgRCNDACW3bGlasQugwCMf5ebNm60IEk0AdeHvuFq7OwXR4JDH8tprrw2aIPJQkAUvPFfpHDdtKp7jzgv0UI88Dkal5g4EhtiH+MrFxUsSjztEFe5uFJUnE2Xf5aqgLWIWXp8IR9yx4IWBSDN06FBDZW28/UraeNHxIYnQjPniXEmPpTTPh+jJnQnuZvAhj0hYqVIle625VvAh6TPCN3lQfOMD/4orrgjET7xFuQOCJy1exHxwI4wiHvKBSPJfJ3jXr1/fhgQg6M2bN892y/Fsx0I/MO1G70+yx3tdFbl61FFH2fctQjDen7ymu3TpYo/z72oV1VGq3qfuPLxfeS/xpYyHOR63eLASVsH15AvLFS8AAEAASURBVNrhvYoXuMv14o7VUgREQAREQAREQAREQAREQAREoCABBDoi7fhNGyp+upZE+PLb2f3WddtDl5FEWRx7cL4i8tel7nPHortgCGaXXHKJ21xgSbRfz549zRNPPFFge6qexDtudB4cxzBE20i/5XGAw2kvND9oqsadTD/8dp4wYYLtgt/6oeKn65vrgvCJMM3vbqIwXXQiqdowHO1w+AvVuTiWgkqRXlfuHCWxLHYBlDyeQMSjLloYPLCcIIRwWJQRlkuSWQyQiFmhoHmBjRw50gqgvMm4k+EnvOVYhFL3BiI3Ju64CLDOSOSKOHrPPffYcGC2h3NbJozZiZ+E3lPBO1Qk4s1xzTXXWBEU7zW8YyO9Sdz5U7HkvAhuqPWEESNGw4ziR5EEafdi5vylUaUsFfOO1ocrrsUdCjwKeVM6Q8BEzObDgHZwI/+IM4RR3sBY9+7dzYUXXuh22SXi9tSpU21YABu4E+QEUDw5eRBe4L/eTzjhhAJ9RHqS7PGR+g23nQ9xDB4IoLyXSYgdj6XqfeqfE/ET0ZP3KktnCNN8WfKBjJHHRAKoo6OlCIiACIiACIiACIiACIhAthFAgwjVHSLNkTog/K4LZziS8Qhn/KbDKYzIQRxOMH57RTLn3BNuPyIofZHeyze0IgwnlkgFktiPVuT0G56n0uIdN1Gcbh7kRY1k5EpFU8ExL90MnQh9CMMpDIe5SIYA7Yx0j04zQjilADdGlCjev6R+JDUdrymcDNNB/GR8/1N9eFZMRg5DBFCEQ9404cQe9uPKjLecn08g0pAQM93F6dGjRyHxk+PwIiXvRO/evQ0h6AgnvOh8VZuwd+dCjQu2L366cyOQEXr88ssvu00FloRTI5BieMnh9RfuQ4iwYQRQRBp3DM+L24YPHx58ULlzMddI4idteBE7bz93TLxLKr3DLdRcuDXbEYLDefu6N0vosal8zuvgtNNOK9Qlohl3lnD/xvhS8QVQPrCdkdA4nPGaRIDjrhCeoCyL8iAO10+mb0vV+9TnwAcoXtS++Ml+RGxueJAbxoVl8JlDe5kIiIAIiIAIiIAIiIAIiIAIZBsBIlwpWBSLxfK7CIGT3/GIezj94Ay0YsWKWLoP2kQrsOwKXTsNxh3kBFA8TKNZOJ0lWvt49sU7bud4wznq5gu70YzfrukogPoh6TgLhjoMRpoTAqgzUgviRYpTIr/DmScPfrPXqFHD1kbBWRHx2nc8c8eX5PL/g/aL+Yyo4e6FjtAZzlz4O5XjEUGLMudtSb/hRCx3PIBx5Xb22muvudVAJGEDIhzCSSTjHJE+MAjxdUVkKHwT7Y1DYlz35sBr1N0xiHTeZLej5vMhRj4Gdw3oE+GRtAAuX2Wy5wl3PAWt8H4MffAh6uydd94ptJ/23IkobkMYj2Tubgb7XREg19b32iXhL+JmqJH2gS+OmTNn2lwpuSh+wiQV79NQtnzAUlUunPE+du8vPHhJ5CwTAREQAREQAREQAREQAREQARGITgDBCi2Dego4ePFb3YmfpPYj8pF0g0VZLHpOaB9OlyiqngmCWnFZvOP2Cxv7Wku48ZFDNR0ttB5KrGP0584xaG44O+JI5nNcvny5LZyFcx1RsTgmlqaViAcoYhBenQh+KPsIcn5BIt5UfhGYooDg+enyJ1DEh9yN0cz3KP3xxx+DprzJXJEXPPwIgY9kfBBwNyL0QtPe3xZLuDi5SsmbwV0PKqBFE0wjjSfW7QjAhFvzhsMbDmGR/IiENZOvknBtkhkTtp9rFs213i8MFeref+CBBwaoEO65a0I6AzxHSc7s3LvdMmicYyupep+GYot23WjrX7vivsEQOjY9FwEREAEREAEREAEREAEREIFMI0DNE1eoBxHyuOOOMwcccIANc0bjcJrLs88+WyxTQx+iGHFRApkLwS+WQcTZqR+RiCYUTVPxNaM4T1OszX2P28mTJ5tu3brFdL5wDl7objxIs4B4TvpFoqipsYLhHIWWgha27bbbxnSeVDcqEQGUQSMQIYAiwlEN3Q+Dp+IXhvcWHpJFmS9iuoSr0Y5B2HTJdnnhubBYCiQ5i+VOxi677FJA7HTH+qo5yW1Di+a4dm7pe6UxnuIUQBHh3N0GPFjJqTF69GibP5HrgQDMeG+55RY3vJQt6RPWocYbi4JU2MMPP1wolJntrqIY68VhvOGi3V3yXbND3fPJ4UE1t7Fjx1oRm2s4ceJE+3CvYVIpcIeDRNG5aql6n4byK+qun/9hGu71F9qfnouACIiACIiACIiACIiACIhArhJAVHT1VXDoIWo2ktMJtVswou1SaYS2I4D6vyHD9V/U/nDHFNc2vGKdkfqPKvKRLFxqwEhtS3K7PwciddEzkjW8YUlpyGPMmDG2rgqeodRXQeAmPD5SKsFkz13U8SUSAs8gEDadGBQaBu/C34HgixeRBu+r57EIl/SDeImRe9OFNFOR3Zkf1uy2hS4jncsXQOkTYTXaw088XNQdjtAxpOr5RRddZPDMxf7zn//Y8aaqb9cPIiYCbOjDFzdD97nnkdINuL6TXSKKJ3MO8qfeeOONhXK94pnM65ncs6RUoGJ5qr8ckp17SR2fqvdp6Hjd50jodj0XAREQAREQAREQAREQAREQARGIjwDees5xZMCAARHFTzz5KC6NhUZJxnfGwq1JJYgRMv3uu+8WbvDfLXgVpotR4wXnKCyaExx8KaScjkb6OBeyTirCUOcvf8xPPfWULZSEd7C7Rmgd1JdB7yPFYjgjAtoJ7OwnGrm0rMQ8QHGRxSMOF1g/DB6RxOWEjKX6O6D8EFcXwl4UQPdGpZ0TUPyQdxdSH62fSEKWn++Bi+/cw6P15fbFEjLv2qZyCUNctF01dO62xCICp3IMmd4XRZR4wBB3bt7IfCm4Lw9ygz722GP29c0b3n2wlPS83XginTfS6zpS+1i3p+p9Guv51E4Ecp0A/4iSj5rvO/I/c0OJ7yduAPrvx1znpPmLgAiIgAiIgAiIgAj8j4Bf08Ll4vzf3v9f4/9LX+BKdaoxCk/jYMT/sFQUJ0rYd5xiFBQjRoRLFyPS9tJLLzVXXXWVof4JdVb8+jOMEwc5imCnq8F48ODBVqD89NNPbe7X8847r9Bw0d2GDRtmhVxSAD744IO2Db83CGn/8MMPzUcffWQjZRGGQ83XJPyi5KHtivt5iQmgTIQweARQJu/C4F34OzkfmjVrFtN8/TwF5NAsyvhR6MLdET+dl2loHtKi+uFuRDjDXZuLjaFuh6skH+64VG/jRenyZjRt2jRqiDfndncrWPe9Unkui50A15xHv379rPDAm5/0Au6uCNXh2MYNgNKwou7O+SkZUjm+VL1PUzmmTOuLLyR3w6a0BPRMY5aL4+UfRW4kEhIU6a4tAighTUWlkMhFfpqzCIiACIiACIiACOQyAfIyEh2JTkOquu7duxuKUyNu8b8lwtigQYMCzQNW1HtIpe2555723BQqRivq2rWrTZdHlXvGxe9r9kX6XzeVY4mnL8RDCkYRFXzWWWdZxyiiRUnV+PHHH1tRl9orzpKJQnV9pHqJ4DxlyhSrJTEfopQpiMXvUH5noNkhTjsvVopk+c5zffr0sQWQuE7nnHOOvW787nCGLuJEVRwzSlMALbEQeCZPEl2XU8CFwTsB9NBDDy2k8DtgoUtfWOHFBOhoRr4F18Y/lqS1vKkxVGvXJlxf7IsUro4A6sx5s7rn4ZYo5l988YUhf0a0c4Y7Ntq2J554wt5xQJl/9dVXozW1+/w3op/7ocgDc7wBaRS4htzlCTVEBoR+PiB48zsraTdv97rm/HxoRTL2uTwukdokut1/ryXzPk30/NlwHF8sfIHy4PNTJgKhBMjZxPco77Fo/xBy157PIW7IFJfXd+jY9FwEREAEREAEREAERCD9CdSvX996MjJSnGM65OeyRHg87LDDrFNVixYtzHvvvWdOPPFEG+5MOwQ/P6Ug25K1U045xXoZ0g+FnLlxjzDKb6IjjzzS/m5FiE0nQyQkGtT9VsMzEvEWZmeffbb9Hx3BsHnz5nbY6ejUgifr448/btP78Tth1KhRNnps7733tuyPOuooM3v2bDt+avmE1o8544wzDCIoxu8NCpWjBTBnriGOYER+IwrPnDkzZt3PdpjiPyUqgFJYxiU7xX2ZUHiXDDbW8HfmT+7Khg0bWhSAnDVrll2P9OeRRx4JdvleeIixzusUcZM7DZGMc0SqOIb3n7NnnnnGbNq0yT0ttERwwk0acYzcCQivqbLQ6uTR+kWAdYIunrC+N2y047TP2Df3mWeeaS6//HL7BRGJCVXhnRGW6pvvzp+IGFHU8c5rkHP6uTj9MbBOAuKiPETduaKJK6H98jxV79NwfWubCIiAMatWrbL/jPphS0Vx4fOAmzepvPlW1Dm1XwREQAREQAREQAREIL0JIHpRLLly5cp2oGgfOK1xE33fffe1giQiGV6YGP9/Tp8+3a6n8s/w4cNtNXpXw2XhwoVW+KxVq5YV6RDb0s0oGEWeT5yg0Hh4znhxYsFJjULZzvMznnSJJTlPPH4XLFhgvVhx6uK3P859LuUkzoMTJkyw8/GdrdwY77//fusJS05RfmcQmTxv3jyzYsUK+5o65phjrAexr5G4Y0tyWaIh8EwM77jnn3/eAnXKMUowKnE8dv7555sLL7zQHkLCWd6U7k3i98OdCieQEvp+xBFH+LttNXoXvs5FI3TcDw2nMW968jlEMsaOBysfEHxQ3HnnnVbkDPfCoCiOcxfnLgEvpFQZQix3Rwj3xz2Z/BhU2wo18p3yAefspJNOcqvBknB/d0cHHtwBSpXxGnAep6nsN1XjK6of7ohNnTrVelFR0f7cc88NewjiojM3X/fczxvrOLt9sSyLOt4/34wZM0y3bt3M7rvvXqDrRYsW2S+XAhvDPHHnQthHwHfFs8I0LbQpVe/TQh1rgwjkOAHSlpBaIxEhkxsy5Cv2b97lOE5NXwREQAREQAREQAQyhsCjjz5qeMRr0VKf4axGrkpClckDSnQRRaDROnznGn7Lh/v/c+zYsYZHUYa3ZDRDKBwyZIjVeqi1gZCG1oOw5izc+dn38MMP24dr55Y4vkU6JlXj5jfzFVdc4U5ZaOkKcTuBuVCDODeQazQ032i0LqIxcMchfKKJ8cBpAqc5PFbxEMaj0zlGufb+Eq0NJz+8XvEOXrJkidWTeP2kUvPyz5nIeokLoHhcctERfYCC8SaK13ApRohC3ORNQfEhxBbcbPFmxLOSMHD/gwGhCiXeNzxCUbvJS8BF5kWEsMo4efPhpXr77bfbamT+caHrfFAgeCEQIfCSi40cELhs86JhPORVIHcFhojEiyOVxofW8Pw7JriFo9hzpwHFHREUoRHhFRV+3LhxgTcrIiyu7KE2adIkOw+2cxcDr9VUGW8ePzw6Vf2WVD+4tD/99NM2byqvLwTyXr16WZdxPhTwysK1m+uN8WHI3R/ffJHdJXLmQ6lt27aFKsv7x7n1oo5HACXvBiIHCapJzIwISn5aBHKEE8aIqMn7JZJ3M+dz58JTlS+jNm3aGNzkjz76aDeciMtUvU8jnkA7RCBHCXCTK5nk89wA4aaI/w9tjqLUtEVABERABERABERABP5LAO0C0YpHaRp6CZqMi9gtzbFEOjeiKoWhEPi6dOlifyeHa4s2xANDzM0EI82jn+ox1jGjh9SpU8c+Yj2mJNuVuACKVyTC5bPPPhvMM57w9+Cg/BVEOV50hK6jqN9www12N+fww4oRMhFIw3lDcgBJXxGISFKLZwwJX52LsrtTgKsuVePx8nT77Mn++weBkUrfeFbSx/z5860Qye7Q8fB85MiRVqDy+0jFOh6sJK5F5IQBLtc8ypYta93U/XMgfjL3cPPx22m9IAHE22uvvdZceeWV9vWH4M0Djrzh/dce3s28JvwkwfSGMM4dNV4reHLhSYpxnWL5oInleAR2XteInHzg3nHHHfZhT/TfPwMGDChwfn+fW+cmgfOS5j3Cg5sYsQig9JGq96kbj5YikOsEyEPsbiAmyoLvNu7uN2nSJNEudJwIiIAIiIAIiIAIiIAI5CwBfv/jMIfj08SJEw3h+uGiJXGMc44LoRHJOQuvlCa+VWmcl2S6zurmuzI3aNDAPY1rSf4EhESK/vienU6AQoyiwtT48eODZL3hToAHzK233mo9Mp1LMj8OeXAHBDWffA6sRzO83cg3euyxxwbFnmjvj6dTp062TXFWiicxLW9Al4iXMfg54hDjEMbwbFXuT+jEb3hqkgPDzynL68Vd6ypVqljPZtq4fLX+WfhghP9ee+0VFOJiv7sz5LcNtx7L8Vx/qvghivNe8A3PL1z0Tz31VH9z2HVeT9zZcp6gNMKD27nxhz3I25iq96nXpVZFIKcJ4NmfClu2bFkqulEfIiACIiACIiACIiACIpCTBDp37mznTTRxjx49rBjK/+roL4SQE2H82muv2TY4ArqaODkJKw0mXSZftIleQj0NBhnrEPB0o6gSXnWIfHjS+aJNLP3wQiXfBT8M8d5DnCW0PBEj1Jjx4K2DQMsDL9KSNJLWLs4PvydNADlScc+Ol0lJjjcTz0VOVXKm8kGHdy/h56Een9HmxWuOD0yEQoTTULEy2rHsi+V4UjNQud4lsU40+TLvLd5neDy73KBFjS90fyrep6F9Zutzrq1LTwBvd4Mm1vkS5gxvWfER4L3lbpYV31kK9sx3C587qTBCVPjcykbjTru72453faLf5dnIJl3mRAQErz+uj6zkCfD/SqTv8rVr19q0ToyK/x/1/in56xPtjNxw538y/qcvykEjWj/aVzwE+F3A/wb8T5+J9Q6Kh0r69Mp3D84cXJt4f3elzyw0knQhwP+apHR0VdIZl4uw9aU2ClYTBe077qXLHHJpHFklgObShdNcRUAEsp9AsgIo1Qhd0bXsp6UZioAIiIAIxEOgXbt2EW+KSwCNh2TJt5UAWvLM4zmjBNB4aJV8WwmgJc8828+ICEokMCnnXA0O5owQuv/++5s+ffrYAkG64Vv6r4TEXBtLf9wagQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiUGgEqoPfr188+KEZN9C3enzVq1FCUS6ldlfAnlgAanou2ioAIiEDGE9hvv/2CEOCMn0yaToA0I/yjU7FixRIbIWlNUpW/s2XLllkbAo+HBw+MXN/hktKX2EXTicISIAQRb4iSTg8UdjA5uHGHHXbIwVlryiIgAiIgAiJQfARIqxBLUePiG4F6jkZAAmg0OtonAiIgAhlMQPl+i//iId78888/ceX9TXZUCK6pEEDJKUuu62w1xGmXK5W8xxLZ0vNKk7+wJG8gpCcFjUoEREAEREAEREAERKC4CUgALW7C6l8EREAEEiSAd5SrGkgBs0MOOSTBnnRYNhGgIAmhNq7AT6JzUxL2RMnpOBEQAREQAREQAREQAREQgUwjsFWmDVjjFQEREIFcIUARpBUrVtjH+vXrc2XammcRBKiaveeeexbRKvpuwsER1WUiIAIiIAIiIAIiIAIiIAIikAsEJIDmwlXWHEVABERABLKKAOJlMikOmjZtarbZRkEgWfWi0GREQAREQAREQAREQAREQAQiEpAAGhGNdoiACIiACIhAehLAC5QCRhT3idcaN26c1bk/4+Wh9iIgAiIgAiIgAiIgAiIgAtlPQAJo9l9jzVAEREAERCALCVA8pn379qZ69eoxzY68oQcccIBp2LBhTO3VSAREQAREQAREQAREQAREQASyhYDi37LlSmoeIiACIiACOUcAUbN169Zm5cqV5rvvvjOrV682VIn3jernu+66qxU+af9/7J0JvFTj/8cffkJpT4uotChtlvilErL9KvwQLUSWlLSKypJ/VFpslSWpHyJLkYgSIVmSUiSVVFqRFlooabH8vZ/f7znOTDP3ztyZO/fcmc/39Zp7tud5znPe59x7Zz7zXWQiIAIiIAIiIAIiIAIiIAIikGkEJIBm2h3P4noXL15sunbtaooUKWKmTZuWRcvQQ8uXLzezZs0yy5YtM6xjlSpVMscdd5xp27atKVy4cGiHGLd++eUXO5+tW7fa6td9+vSJsaeaxUtgw4YNCYfEdujQwYow3O/x48fHOwW1FwERSIBAmTJlDK/ffvvN7Ny50+zZs8cQJl+wYMEchcknMBV1FQEREAEREAEREAEREAEREIHAEZAAGrhbkjcT2rFjhxk6dKj5888/45rApEmTzMiRI83vv/8e0m/79u3miy++MFOnTjW33367adSoUcjxWDZGjBhhPZpoywd6WfIJ/Prrr2bcuHFm+vTp5tVXX03oBFQp37Ztm9m3b19C46izCIhAzglQ2Kh48eI5H0A9RUAEREAEREAEREAEREAERCANCUgATcObGu8l4Wl58803m2+//TbmrgilgwcPNm+99Zbtw4ducsudeuqpZteuXebDDz80X375pUEI7d+/v3niiSdMxYoVYx5/5syZ3tgxd1LDuAl069bNrFixwhQtWjTuvuogAiIgAiIgAiIgAiIgAiIgAiIgAiIgAvmBgATQ/HCXcnGOS5YsMffcc49Zt25dXGd54403PIGyQoUK5qGHHjKlS5f2xrjsssvM6NGjzYQJEwxehvfee6959NFHveNZrZDL7oEHHsiqiY4licDPP/9sRzrggAMSHrFXr15m9+7dBjFcJgIiIAIiIAIiIAIiIAIiIAIiIAIiIAJBISClIih3IsXzQJT8z3/+Y15++eW4w9737t1rHnvsMTvjQw45xBCq7hc/OXDggQeaTp06mY8++sh6lpJflPDoEiVKZHmleJYOGTLEEJJP7jrmKcsfBCjEIhMBERABERABERABERABERABERABERCBoBE4MGgT0nxynwCh6VdffbUhfyeCI95/7dq1MyVLlozp5HPnzjXke8TatGljypYtG7EfBTiaN29uqw/XqlUrphD7F1980Xz22Wd2Tt27d/fGTYaHojeYVkQgnxA49NBDTeXKle2LAjcyERABERABERABERABERABERABERCB+AnIAzR+Zvm+x8cff2yo+o2VKlXK3HnnnaZevXq2EA77shMb33vvPZpZO++889xqxCXCKq9YbOXKldYrlbZUjz/++ONj6RZ3GwTg119/3Vas37RpkylUqJAVaala37p162wrJtOfokFr1661qQOoel61alX7uuCCC8zhhx8edU6kCsCDFk9YqqZHM1hMnjzZHqaAFLlV/UZ6AbxkEZbPP/98m2v13XffNaQ04IX4XL16dVO3bl1z8cUXmwIFCvi7m0ceecSGqzshG0/b+++/37YhpQEpDLDPP//czJgxw67fcMMNhnyxzz33nJkzZ45BnKtfv75tiwiORzHjsd8vXtvOvh9UqZ42bZr56quvDNe5ceNGmx+2WrVqNo/s6aef7mu9/yoFt95//33D9a5fv9788MMPtvo1QuHRRx9tmjVrFlWU33+0YO8hN6u793hEy0RABERABERABERABERABERABERABOInIAE0fmZp0QNhBe/NSy+9NFvBL/yCly9fbnfhkXbkkUeGH87RNqLgwIEDbQVxhLDrrrvOE2lzNGCEThRnonATBZr8Rh5MRLhPP/3UvPTSS7Zq/WmnneZvYte3bt1qRo0a5eU+dQ0I7aeAFKLcxIkTzY033miaNm3qDocsEf4QG+GWlQCKQD1lyhTbF5HaiWBusLffftsKf+TcRLymiNV3333nDtsl4iBiNW1he8QRR3jHyeG6c+dObxv+7nwnnHCCJ4CuWbPG29+iRQtz2223GURjZ998843hfiHCvvPOO5YjgnA0ARTRmLl8/fXXbgi7JEUCL0Tfs846y/Tu3dsUKVIkpA0bFNVibMbxG2LwqlWr7K4XXnjB8jj33HP9TbQuAiIgAiIgAiIgAiIgAiIgAiIgAiKQoQQkgGbgjUdguuKKK6znY7yXv2fPHk9oO+aYY2z3P/74w3odLly40HzxxRe2CnyNGjVMnTp1rJhFPtDsDI9GxDY8Ffv167efx2J2/bM7vm/fPtO1a1frcUhbvOmaNGlivSRJA4BH4/z5861XJVXr8ZDEu9IZXoeIf3gtYocddphp3Lix4ToREvG6nDdvnu0/aNAgex7Ol9uG6Nm5c2ezZcsW68WK12yxYsXMokWL7FyZ97JlywzXNGbMGG86eO5yLxFHEWQPPvhgm66ABniARjKKWDnxk3vKfScH7Jlnnhmp+X778CZF2ERsxWB3yimnmHLlypnVq1ebTz75xArJM2fONHjZPv300wYx1Rmeo9wDJ37i3YowXLx4cfvs0J9j3A9EVjxRIwnZbjwtRUAEREAEREAEREAEREAEREAEREAEMoOABNDMuM8hV0m4dk4NT0kEQwyhDVEKwY9wZL8hCFJgaerUqeaOO+6wIcr+4/51hEc8L7Hrr7/eVKlSxX84KevPPvusJ35y/XfffXeI0NeqVSuD5yAiHwLdsGHDzJNPPumdm7BvJ34i3CGwlS9f3jvOCh6gQ4cOtQIwnqDnnHOOFflCGiV5Y+nSpXZEuF1++eUhFdgRBG+99VaDCEo7PHeZO+Y8NGmDAIogjDiZleEhyzXj4XryySd74eukEMjOeE6GDx/uiZ9dunSxHsh+cRxBFu5vvvmmFVqfeuopb56Mj5CLMIrhudyzZ0+77n5069bNPP/88wYxHcPbNhEBlOfcPevuHKleIjI7Yy7+bbdfy7wl4J4T3Zu8vQ+Rzu7//dXvTyRCeb9Pvz95fw+izcD/+8PfN/2Ni0Yqb/a7+6G/bXnDP56zunsVTx+1zV0C7u8b94bUb9mlf8vd2Wh0ERCBVBKQAJpK2mlwLsLIneE1iEceIho5J8m/eNRRR1kPUTzxEN4WLFhg2rdvb8aNG2fzjbq+bkn4OWHp2IknnmhFMXcsWUuEtfHjx9vh+Ac3YMCAEPHTnYe8l7NnzzZ4sq5YscLm96xUqZL1KkSMw/AqvO+++yIWjMKjlByZ99xzj/2ggJiH12Vu/1MlR2ikPKt4Vzphl7lTvMoJoGzHa1wHYja5UjE8ZP1eslmN99prr1mOtMH7FLE23PAm7du3ryGsHqETAf3CCy803AMMr1ZnZ5xxhlsNWbZs2dK88sorVmjFExQxm+c0J8ZzQ3qDoBjpDnjJgknAeUcHc3aaFf9reMmCR4D3Ff73FsGboWZElIksmARIDSQLLgEENr0/CO79oY4AkWTKsx/ce6SZiUCyCWQfm5zsM2q8fE3A/yEF707ET0LhEQgJWcYblOXYsWNt5WoulsI4Dz/8cMTrRkzkjTUh5YhruSEW4rmImIWdffbZnqAWaUIIc+S6xMsRQQ6jP2IuhqBYsmRJux7pBwV4nGCHxyi5QXPbSGcQzZxYyXFX8Cha2+z2I576x8uuvf84oqSzjh07utWIS3LTYjAnr6gzf3EpBFUXSu+Os+Se4X3Lszly5Mgci5/+MbUuAiIgAiIgAiIgAiIgAiIgAiIgAiKQvwnIAzR/37+Uz55cms4QqBClEJrCw6AJY0f0RFDEE4+8jgiLFNhxRiGeDz74wG4Szkwl8dwwPDqd4WWaleFNyctveCQ6a9iwoVuNuMQTtkGDBtZ7lAb0rVixYsS2ydqZVSEqil05Iww9EcO7NyfGc/L999/brhRi8guZkcbze5X6Czv985//9JqTcgEvXQRtPF1r1qxpvZBpwD1IhhGeT07avDQ8B5z4zpcDBx2kP9l5eT8indv9XuneRKKTt/v43XGhh/xd8KfcyNuZ6eyOAO8puC/J+rvtxtUycQL8bXNhovx9y40vqBOfZeaOwL3hHulvWzCfAf/npbx+LxlMQnk7K/f+mr9tem+Qt/dCZxeBVBPQp+lUE8/n5wsXOinAE77PXSIhBVdeeaWXk5ECSU4ARRB78MEHbVNCx/GczC2jeruznIisa/8K53fmr6Tu9oUv/W384ml4u2Rs86YqK49UvyjjhICcnjenAiihP04k+vHHH60QHusc/AJoiRIlbHV3nhuuBe9avI15UTG+fv36Vnwm7ycexYkaofPZibWJniO7/ni5utBD0i/wOyULFgHCD3m+8/pZCRaVYMyGL9927NhhJ0NBtWj/q4Ix28ycxebNm21qGf+XdZlJInhXzXsnFz3D/1//+4ngzTbzZsQXPPz+8LvD+wNZsAi4mgmIa3p/EKx7w2yof8D7t1KlSkkADd7t0YxEIFcJSADNVbzpN3j4B0iqjmdlVOp25oRE3rRRhIh/Prxxu+GGG7wPqa4tS/JpOuObVPdBlm+7w+fh2kVa+vMjlS5dOlKTLPe5MHbOyz/K7Mx/Dt4A5abBIVVeGf7riuea/CIm9xERNFZbv359SFO8iBGx8Tp294UGPBt4hfJCuKRIUqdOneRVFEJPGyIgAiIgAiIgAiIgAiIgAiIgAiKQmQQkgGbmfc/xVftFMATBMmXKZDmW32vQCWHkoqRKPEZRCooPZWcffvih4YXhRfrII49k18U77vcswSMoXqM/oh3CLaJtduKrv9AGXkeRzIWVRTrGPs4VNMtpiIjfM4FQ9aZNm8Z8aZE8TlyaAirbU9iJPLTkW3VM8ZqcMGGCrXp/7733yjMiZtpqKAIiIAIiIAIiIAIiIAIikEkE+GxOnQ9XPJZIOv/n50xioWtNfwISQNP/Hif1CosVK2a9IAnLRaTDQ88vcoafzHltsp8w5bww//yo9peVIaJxXX7hjf6rV6+23TZs2GCqVq2a1RCGNs6ihSy7kHDXLnzp934NP5bftitUqBAyZbwzk2GuCn379u2tkD5//nwzY8YM89FHH9nhFyxYYNhHSLxMBERABERABERABERABERABETAGKLyVq5caetV7N69ez8kOPxQ2Je6Hv7Pxfs1zIc7uHY+8xM1KMs8AhJAM++eJ3zFCE+zZs2y4+DJ6RcYwwd3Xp/sd7kx8Qi86qqrwpvut823UVT7xo4++mhz+umn2/Xy5cvbZaw//EWC/POJ1B+vwi5duhg8XRHqKOLkF/AQQrMTQFetWuUN7T83O90/kEgVzL1Of624okH+ffl1ndxheMLifQsbxF/HIdI18U+YfLE8L4S7U9kdYz85VUlpQL5Pv/EtJQWReD3//PNe3lm8Q/OzAEp+rxdeeMFeKt6z559/vv+ytS4CIiACIiACIiACIiACIiACMRPAIejTTz+1Hp/ROuERSoTdmjVrDIVos6o5EW2MZO4n9Rmp0OIxrtFv7733nhkwYICZN2+e/VzJ56qpU6f6m2g9AwhIAM2Am5zsSzznnHM8AZSci4Q0R8tDOW3aNO/0TojiG6WOHTt6+6OtIHY5ARTRMZY+kcY69thjvd2M17Zt26jVvQmppsAOhXuceFm7dm2v/3PPPWdFtmjh4Ah8s2fPtu0LFixoTj75ZK8vK1w7XrGIeAi8eNSGGwLhxx9/HL47V7bddSRaICm7ycEQMRLhd9KkSVmmPeAekeMTO/PMM83AgQPt+jXXXGM9jkm9wHMVrdARVeFHjx5t+yAg5ndzof1umd+vR/MXAREQAREQAREQAREQARFIPQGiNz/77DMvdVh2M8ABhc+2OJ/kpJhwduPHepyifMw7p4YTFM5N27Zts444NWrUMDiXyDKPwIGZd8m64kQJnHrqqd4fQATDMWPGRBySP5bOU5RcoXx7lBeG92jz5s3tqfnGa/z48RGngdfliy++aI/hpeq8DBFuXbEnPEDJLxnJ+KbMVbbnOEKx81507Y855hi36ol83o7/rZC3kpCEVBgiLcbcXbXX3Dgvha6c2Dp27Fj7bWKk8yB6P/PMM96hli1beutNmjSx66Qo8LfxGvxvxS8e+3mHt9O2CIiACIiACIiACIiACIiACGQCARxwSBEWr1MFjjJ4U+aklkZucH3//ffNwoULs335z83nQ8TPcuXKGRxk8G697777/E20niEE5AGaITc6mZeJqHfLLbeYPn36WG9JQo7JWXnWWWcZqr6TH/Stt94yTz75pHfaW2+9NU8rcl9//fWGP5YUMXriiScM3wK1atXK5jXhmy28Ex999FF7nEn36tUrpHhOz549zXXXXWevF+9CBEr6V6tWzfYhFQD9XWVy/rgSSh9uF110kZejcvr06VYgRWAlzP7zzz+3hZ74A021eTjmthGejiEqco8aNmxow9WTHWoNJ8IWXn75ZcurQ4cOhlfjxo1tCgX+oVLk6vHHH7f5PJkTgudxxx3HqrULLrjAvPLKK7Y/IjbFplq3bm3ZIa4ibhPGgJcuhrgbb6iE7agfIiACIiACIiACIiACIiACIpBGBEgxltOoPyIUFy9ebD8r5jUSIgsPP/zwuKaB5yvG50v3+TeuAdQ4bQhIAE2bW5naC8E7csSIEaZ///7225RXX33V8CK3o7/AD6Hx3bp187wpUzvLv8/GH8khQ4aYQYMGWWER8ZEX4dSIf35DVGvWrJl/lxU6yRnywAMP2NB1iu3witQfj9PBgwdbITFkkL82CM+mirnzUiTc24X5u7aIoZynTZs2bleuLRFfXX4Uwgp4Ubgp2QIoF9CpUyebcHvKlCk2FH7UqFGGV/gzQ1tSB/Tr149Vz8g1e9ddd5nbb7/dfnP5+uuvG148Ywig/vtI/lDud7z/HL2TaUUEREAEREAEREAEREAEREAE0oAA6d3wAE3E8JzEizI/CYg42axbt846P3HteL9++eWX9rMjIfA//vijl/qOVHUzZ860IjF1JcIjOYkWXbRoka1JgXMPjjqRPmvixLRx40ZD3RJY4bRD3lEiHenDSwWYEnkSE+srATQxfhndu169eoZw5mHDhlnhDO9Kv/iJNygVusPzYOYVNOZB6DRh6lQKZ75+0QzhskePHlFD9fnGiD9Y9Cf0P7w/Yf54KVI4iRD6SIZYd88999jCNnjOkgfUGX90YYp3LUV9UmGXXHKJFYTxnOQfGpZVftJE5oRHJl7DcCTH59q1a+0/GP8zwz8KCmSRVzZSoSTSL+AlOm7cOC+9Av/I3H0kQfeJJ55obrrppoj5VROZv/qKQH4gsGzZMkOSd77pxrudLwNOOOEEc8YZZ0T8ncoP16Q5ioAIiIAIiIAIiIAI5JxAsgrsMk5+EkD5zO9S4UGPdHe8+FxKCjg+V/bt29c89thj9vMln/ExCiIT2YkIymdkHHOIAnWfOWmDIxRRo9Sr8IulTz/9tOndu7dtzzlY93vekn904sSJVldgHFlqCRzwl3jwZ2pPqbOlIwEeI/5IEBqOkFexYkX7rUdQr5X58gecokUU06lUqVLEb3CizZ/+GzZssP3540fIOwKqy3MZrV/4fr4d+vrrr21/Cj3F2z98vES2+VaPP9IkuHa5QRMZL7u+5BzlmzDSEeB1ivjJP5tYGVBMCn58o8k9IN9npG/hsptHkI/DxuWc5VtKBHZZsAjwhQEifl4/e3hW8+aMb6YjGW9WSeXBG7VoBcQi9cvP+/jWn78TGAXn+N8kCxYB/u/whWGqvvQL1tUHezZbt271coPzvznSl5LBvoL0nh0fwvn94W97tC/d05tAsK+O96d8VuA9bV4Wjgk2pbybHU4svH/j3sT6uSPvZpv9mfm8wPOWleH1mIx6D7yHRMDLyvhcxue6ZBoag6vtQNqzWN934/1JlCqenbxXxpmGNHb8T+vatasZOnSoFUAZm8/kderUsU5OJ510khVK4UpdD/pzTThLcf1Lly61TlHMBecejrui0DiHIXoyFtyJ6sQRAeeEZ5991nqkopVQWwRWstQSkAdoanmn7dn4hecXmVd+MOZLlXdX6T3eOdOfP4KJ/nFHOOUVBMODNZXGN2X8s3H/zOI9d5EiRQyvnPaP93xqLwJBJMCbeDztX3jhhSynx7fXpJDAe5o3gORPkomACIiACIiACIiACORvAtSR8HsY5ubVUPeDQkpZGV/KJPoZOavxed/r97iM1JYUfIiOODndeOONNv0a73+JUGU73BA/H3roIStwcowoKoyoRcRNHJVIYec+L1988cXm6quvtuntqDOC1+e1115r+7gf1AjBw5S6F85oQ2QWTkCk0yPqUZZaAhJAU8tbZxMBERABERCBpBDYt2+fDev54IMPYh6Pb5vJQ0xIEG8CZSIgAiIgAiIgAiIgAiKQXwiQui07I2oOATRWI2VU9+7dvebOs56wd4wwdyd+ukY4UlEPpWPHjjYlYLgAWqVKFVtE2bVnyT5S6s2ePdusWbPGf0jrKSIgATRFoHUaERABERABEUgmAULa4xE/3blJxn7hhRcaqoEq9NhR0VIEREAEREAEREAE8h+BY489NtsQeELI+eI8UUMYrFy5cpbD5HbKFOpoZPf+FS/LeIywdhfC7vqR4grPUCyap6bbTztSk/hD2iONyVhEzCKA7t27l01ZiglIAE0xcJ1OBERABERABBIlQK5P9610TsaiCNm9995rBg8enJPu6iMCIiACIiACIiACIhAAArGkA+PLb3JQJmp4SlavXj3RYRLq/69//SvmHKCxngjPzHCjVgiiceHChU2pUqXCD9ttvEALFChgxUzyjfrHgVUkcxXgs8vbGqmv9iVO4MDEh9AIIiACIiACuUGAHKennHKKfVWrVi03TqEx8ymB++67L+F8T+Q6IoeoTAREQAREQAREQAREIH0JRBPj4r3iZI0T73lzu32knKIImxieoNHESo7xwlx7u/HXj3QosOWuJZ2WEkDT6W7qWkRABNKKQMGCBb1CUUEplpVWgPPpxfBt9LRp0xKePYnsScAuEwEREAEREAEREAERSF8CFCXCkzERK1GihCldunQiQ+SrvhRQQtSkINKmTZsizp1iRk4czSQ2EWHkk50Kgc8nN0rTFAEREIF4CcyaNcvEkig83nHV/m8CvCmi8mahQoX+3pnLa1u2bDHbt29PylmGDh1qeE7S0cit5PIr8c1++Dfz6XjN+e2aEOHJFRbJ8yK/XUt+nC8FIs4///z8OHXNWQREQAREIA4C5Lek+A6VzHNieDNmWvFM8nnWrFnTkHbqhRdeMOTeDzf2Y/Xr1zeucFJ4G20Hi4AE0GDdD81GBERABJJGYP78+eb+++9P2ngaKP0IzJkzx/CSiYAIZB4BvryRAJp5911XLAIikJkE8FCsU6eOWbJkSdwAKCqEB2imGVXeL7nkEjNkyBBz1llnWRHZMfj000/NAw88YDd79OjhdmsZcAISQAN+gzQ9ERABERABERABERABERABERABERABEUiEQNWqVQ1FeBYuXBhTLnmiNOrVq2eClPsz1rmMGTPGtG/fPhFcpkWLFqZVq1bmpZdeMg0aNDBt2rSxRaC++uor8+KLL9pIowcffNBcccUVCZ1HnVNHQAJo6ljrTCIgAiKQUgIXX3yxUfGk3EW+c+dO+wayaNGiuXsi3+jkIbr++ut9e3K+2q5dO9OyZcucDxDgnhR4ckWeDjvsMIVZB/BekcqBD2KpTCERQAx5NiX9f8gz9DqxCIiACOQZgQoVKtiq5suWLbOV4YkGCDfCv2lXo0aNwIV2u6JD4XMO3450XeFtYtmeOHGiGT16tLnzzjvN008/bbvw3oWw92uvvTZhkTWWOahN8ggc8FfS1j+TN5xGEgEREAERSBYB8heS7xGjIFLx4sWTNbTGSRIBBBzeiB1++OFJGjG2YUjMTuL1RO2jjz4yp556aqLDBLI/4vSOHTvs3IoVKyaRLYB3afPmzfaDVSq/QAgghkBOaevWrWbPnj12boRN4gUkCw6B33//3fD7Q0iq8s4F5764mWzcuNEWRiFvYtmyZd1uLQNCgC9Hef/Gvcn0St28h/3xxx8NObkpsomwR7GkUqVKGURQWSiBDRs2mHXr1tlQeH15G8omv2zp3Ux+uVOapyWwfPlyW7CDb6xYxxACSOrctm3bmKrbffjhh2b69On2jxdvUPhgXLlyZXPuuefal/7YW6z6IQIiEGACeG0OHz48oRkeddRRpmHDhgmNoc4iIAIiIAIiIAIiIAL5kwBfbpUrVy5/Tj4PZk34fawh+HkwPZ0yBgISQGOApCbBIDBp0iQzcuRIw7fefuMbvC+++MJWu7799ttNo0aN/Ie9dbwZ7rrrLpvzxNv518oPP/xgX/PmzTPvvfeeGThwoEIl/YC0LgIiEDgCt9xyiyG3Ed/Y59QI5cl0z4ecslM/ERABERABERABERABERCB/EXgwPw1Xc02EwmQpWHQoEHmoYcesuIn31SRhLhXr16mc+fOpnbt2hYLQmj//v0jhoXi3v9///d/nvhZpkwZm7OjX79+5oYbbjAkhMY+/vhjc9NNN3lhk3anfoiACIhAwAgQtuUqT+ZkameeeaZyFuUEnPqIgAiIgAiIgAiIgAiIgAjkSwL/+Esw6p8vZ65JZwyBN954w4wbN85eL8mYx44dayjucuyxx5q6deua8847z+apWrJkic3Ft3LlSnP++eeH8HnyySfNO++8Y/dRye7RRx+1iYsRPgmfv+CCCwzh8KtWrbI5lcgbl6558ULAaCPQBPB2dkVcChQooDxfAbxbu3fvtkWQ8iIP0Mknn2y/rJkzZ05cZOrUqWPefPNNQ2GgdDZy6PLCyJHH75AsWATwYOZLzUMOOSRYE9Ns7P8eF3HD3wp5iwfrocA5gN8f8oMrP2uw7g2zIQc1dsABB8SUnss21o+UEcAxhvdv5LrkHslEQAQyh4A8QDPnXufLK+XD62OPPWbnzgekESNGGJLx+4035Z06dbKV6ti/ePFis23bNq8JbxL5sI8xBp6g4WIFbx5vvvlm700KOULdB2dvIK2IQIoJuMTkJCd3xVxSPAWdLuAEhg0bZlOD8CE4Frvkkkusp3vJkiVjaa42IiACIiACIiACIiACIiACIpAWBCSApsVtTN+LmDt3rvnpp5/sBbZp0yZqJUUKFzVv3txQ1KNWrVrm22+/9aAsXbrU5vhkx0knnbSfgOoaIopWq1bNbiJ+4hEqE4G8JEDeWsR4XuS5lYlAJAJdu3Y1X3/9tenSpYshvUe4UdGzWbNmZsaMGebll182RYoUCW+ibREQAREQAREQAREQAREQARFIawIqgpTWtzf/XxxFiZwR6p6VtWvXzvAKN3KEvvbaaza8vWjRouGHQ7Z37dplt/EqLV68eMix7DYI09+8ebNthiAR7mXq+pPL1HmXIlhECkPFa/Xhhx+27RB1L7/8ctfdW3755ZdWGFu7dq2taE8YByH9vAjpP/zww7224SujR4+2HoWEwiIcUwjqgw8+MJ9//rlhXM5JqgByrSIoO/vuu+9suwULFpjly5dbsQXR+JprrjHly5d3zSIu8WacNm2a+eqrrwxpChCYK1asaEVnQnlPP/30iP3YuWjRIvPWW2/Z4x06dDAlSpQwn376qZk/f74h9cE333xjx6pevbpp2rSpTY8QdTAdEIE0JHDkkUfa1B6PPPKI/R3md5XwLipV8juc3d++NESiSxIBERABERABERABERABERABj4AEUA+FVoJIAJENw6uJD/g5NcI9swv5xNuOHKAYomm8ggFh91OmTLH9qUQfKYfo6tWrDdXsnUVrt2LFCq/dVVdd5ZrbJfMcNWqUJwi6g5wfz9f333/fTJw40dx4441WDHTH/cu3337bip779u2zYiGFn7Zs2eI1YR2Pw2eeecY8+OCD5oQTTrBCY+/evUOqTuOdi+fZzJkzbaEqBNNIhkg7cOBA29Z/nHQFvCZPnmzOOussw/iRvNMQOB1bxGDWn3jiCf9QhiJYCKWMhbDcunXrkOPaEIFMIMCXN+RG5iUTAREQAREQAREQAREQAREQARH4LwEJoHoSAktgz549Bi8m7JhjjrHLP/74wwpxCxcutAIdHps1atQweDIioOU0ST/iYZ8+fWyVeU507rnn2vPF86Nx48Y2vJQ+8+bNiyiA4rHoNzwpIwmlVKN3dtppp7lVO7/bbrvNelGyE+9RzgsDEq7jDcm5yRc5aNAg62mJN2o0W7NmjenWrZv5+eefTalSpcwpp5xiBUi8KxGDKYBw1113me7du5v77rvPFkXAWxNPS7xdaYfwyL0aPny4ee655wzhtn7DqxRh03m9MlfOU65cOYMg/Mknn1jhFhEV79Onn37ay8XqH8etI/7OmjXLkPaAcSpXrmznAkuEW+aMF1ylSpXscddPSxEQAREQAREQAREQAREQAREQAREQgcwkIAE0M+97vrhqQqQJBceKFStmK7wj6r377rsh80f0I6/d1KlTzR133BExB15Ih/9tIKLiWYiXKeHViHiIanhOtmjRIlKXLPfhJYkgSVVORMhIFi6AfvbZZ5Ga2SIlHKDgE4KhMwRGQsgx9uNVGR56jgfo0KFDDeIwnqDnnHNOyBhuLJbLli2zm3iZduzY0TtEuPott9xiQ8zxOB0wYIAVJfEGJY+qM4rz0I/lhg0b7HUjyDpjHIRRJ37imUkuV79QDXcKuVCoatOmTeapp56ygqsbI3yJ+InoyZxYOuNcvXr1Mgih2CuvvCIB1MHRUgREQAREQAREQAREQAREQAREQAQymICKIGXwzQ/6pbt8nMwTr0I8HxE/ESnJc3nGGWfYJdsYwlf79u1DQrntgSg/+vfvb+6//34bTo0Ih1177bU5Ej/pSyV5FwKO5+r333/Pbs8QARFdMXJsYnhZ4kHpN7wYnTCJmHjAAQfYw4SRIw5ihx56qPXIDBc/OdakSRPTo0cPVg0es4iLTki2O8N+NGzY0JBX029cS9u2bf27LBu/+MlB8ozCzNn69evdql2Se5V5Y+RwJXzdL36y/5BDDjF9+/a1aQfYRsxet24dqxENHrT3i580ZM4I5CwxPEuzum7bSD9EQAREQAREQAREQAREQAREQAREQATSnoAE0LS/xfn3Av0CKN6dCFqEwiMCEiaN2MVy7NixnhhGTkqKB2VnCJ4IjeQFLViwoNecvJJ4Q7pcoN6BGFf83o/h3p7kunRC65VXXumNSIi436h878xfGIhwc8K7sVatWmWZ05SKz4SAY3iMkhs0ml1xxRWeyOpv4/q7fRdddJFbDVlWqFDB28Zr1294YTrze5i6ff4lnqEY1/jOO+/4D4WsU5zp2GOPDdnnNsgfevTRR9tNxsEbVyYCIiACIiACIiACIiACIiACIiACIpDZBCSAZvb9D/TVU6DHGWIW3oYjR470xE53rEqVKlb0pAo6Ri5J52np2oQv8RIk5BoPRcLfCS0nnyTm8mLmRATFA9R5pCLY+s0JolRlJizdtXMh267t7Nmz7SrXQ1i9M8L1neG1mZUxtvNGpZ2/b3g/f4i9/5i/CBTs8dSMZHijOgu/Z84LlmvOqio9/cOrzbsxw5fZFcPyz9s/n/BxtC0CIiACIiACIiACIiACIiACIiACIpAZBJQDNDPuc768ykKFCoXMu3PnziZ8n2tQvHhxg1fl6NGj7S4qmPvFQ9fOLREInWBKSDXejg888IDNKzljxgxbUGjMmDE2zNz1iWXpREtyeyJskpfShWQ7AZQwcsREvBgp+uPPA4pgh6cnhsjp+rK99n+h5KwjKGZn/jbRBNACBQrYcPpIY7nQe45lJV6Gh7S7scjnyfVj5AiNJ6+qK37lxvIvKZ6UlXFNzhQC70hoKQIiIAIiIAIiIAIiIAIiIAIiIAKZS0AeoJl77wN/5eFi5/HHH5/lnOvWresd94uF3s4YVm666SbP03HOnDlWuIuhW0gTFwZP+PXSpUvtMfJ8rlixwq67PJpUU8cIT//hhx/sOuHwv/76q133V3937Vgi3lKxPTujgJKz8NB0t9/vven2RVpGEzkjtXX7/CImwi4iaHYv1zc8l6jbz5JCUzIREAEREAEREAGt2hkVAABAAElEQVQREAEREAEREAEREAERiJWAPEBjJaV2KSfgF/AQ/cqUKZPlHFxhIRr5xbcsO4UdJHyaAktOuESIy8r7May73Tz11FPNQw89ZNfx+jzuuOM8r052+gXQcePG2XZ4izZt2tSr/k7RJxeSbxv89YO5ISCSDgCRNFwgdu3c8ueff3arnrert+N/KzkRNsPHiLbtF1dr1qxpry9a2/D9fs/X8GPaFgEREAEREAEREAEREAEREAEREAEREIF4CEgAjYeW2qaUQLFixaynI8WKEP0QI/0iZ/hkduzY4e2iGI6znTt32r7ko8SLlMJHWVmJEiW8w84b09sRwwqh54io5BCdN2+eue666zwBlP1u/Dp16tjw8927d9sweL8AikgaLnBy7atXr7Yz2LBhgz1HVtOhjTNSBKTa/MWROPell16a6ink+/Ph6fvvf//bXoc/t2m+vzBdgAiIgAiIgAiIgAiIgAiIgAiIgAikkIBC4FMIW6eKn4C/MM6SJUuyHMDv9enPf/niiy+aDh06mDvvvNO8/fbbWY7BQYogOaPqfE4ML1CMCux4YrpK7877k2N4ObqwfvKAcl4nWoaHv9PeLyg6IZT90cxfxCm7wkHRxkhkP0Kvy7PKXFw+0GhjIgRTOIp8pXv27InWLKP24/nMFwG8wgXxjAKhixUBERABERABERABERABERABERCBBAhIAE0AnrrmPgGqpTt79913TVZFbaZNm+aaGr+A+M9//tPbzxhZGUWJXOVyvO9iybUZaTx3fub7xhtveGP6BVD6uTygmzdvNhMnTrRDUXzI5RH1j127dm1vk6r1f/zxh7cdvoLg6KrJFyxY0DtPeLvc3nZz3rt3r5k0aVKWp3vttddM7969zRVXXGEGDx6cZVsdFAEREAEREAEREAEREAEREAEREAEREIFYCUgAjZWU2uUJATwpy5Yta889d+5cQ2X2SIbYN2vWLHuIXKF+0RMvUpfHc9myZeall16KNIQhhH7IkCHescsuu8xbj3elRo0anniKWInhzRdemd4JoBxHKMUIjXdh8nbH/34gqjqPUTxAJ0yY4D/sre/atcs8+OCD3jYiMlXn88JuuOEG4/KMjh07NsS71j8fvD6feeYZb1fLli29da2IgAiIgAiIgAiIgAiIgAiIgAiIgAiIQCIEJIAmQk99c50Awt0tt9ziiWjPP/+8GTZsmA0pJ6R606ZNVjjr27evN5dbb73Vio1uB6Hm/fv398Z49NFHzciRI21f2mzbts3MnDnTtGvXzoZfsw9hsk2bNqzmyPDidGHwP/30kx0DITY8jJmcoC4/p/PodN6jkU7cs2dP7zpGjx5tBgwYYAs24WHJeRCCCfdfuHCh7V6uXDnTpUuXSEOlZF+1atVMixYt7LnIp8rcEG6pfI93LKIznrs9evSwqQJo2KRJE1s4KiUT1ElEQAREQAREQAREQAREQAREQAREQATSnoCKIKX9Lc7/F1i/fn0zYsQIK2IiVr766qv2hbDpzyuJ6NitWzdD+3DDc/LGG280Dz/8sC2oRF5QXlRbRzz0G+Ln//3f/xnGS8QQMqdMmeINER7+zgHOwflmzJjhtctKAEVQRPR84IEHrOBJP154l1Ioym9HH320DSV3eTj9x1K53qlTJ7Nv3z7LAtajRo2yr/D7x5xg0a9fv1ROT+cSAREQAREQAREQAREQAREQAREQARFIcwLyAE3zG5wul1evXj1DCDW5MclpifnFz7p165rhw4eb1q1bR73kSy65xDz99NMh+TD94idh8nfccYcVW3Oa+9N/cubs5sr+SAIo+xH9nCFaZlXpnnZ4SBIufuaZZ3rj+8VPUgC0b9/ePP7446ZixYpu6DxbwqBPnz72/lSpUsXzYPXfv/Lly5vbbrvN3H///VaUzrPJ6sQiIAIiIAIiIAIiIAIiIAIiIAIiIAJpR+CAv8JQ/0y7q9IFpTUBHllCqFeuXGlDyhH5ENDisZ07d5q1a9ea9evXm9KlS5vKlStHzLsZz5h50RYWVI6n6BFeoIS8I6K6vJt5MafszkmFd3J+fvfddzb8n3vHPQjynLO7ptw6jkC/ZcsWOzxCskuXkFvn07jxE9i+fbv9MsblGY5/BPXILQL8nSfNBlasWLH9UpDk1nk1buwEKAB46KGHmqJFi8beSS1TQmDr1q2G/9cY/6OJ2pAFhwBffPP7Q854fodkwSKwceNGm+qJ97aulkGwZpjZsyEtF+/fuDf6/JHZz4KuPvMI6N1M5t3zfH/FhI0jeibi3UhYOMWGeOVngwUCYrwCcF5eM3ldjznmGPvKy3no3CIgAiIgAiIgAiIgAiIgAiIgAiIgAplBQCHwmXGfdZUiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIikJEE5AGakbddFy0CIiACIiACIiACIiACIiACIiACIpDJBL7//nuzevVqQ+oTUjpVrVpVqRsy+YFI82uXAJrmN1iXJwIikH8J7Nq1yyxdutReAPldlQM0/95LzVwEREAEREAEREAEREAEgkCAOgOjR482Tz31lFm4cGHIlEixRpHejh072sK61JmQiUC6EJAAmi53UtchAiKQdgQo4rJgwQJ7XTVr1jS1a9dOu2vUBYmACIiACIiACIiACIiACKSGwGeffWZat25tvT4jnZEiu/Pnz7evBx980EyaNMnwOSQvbc6cOaZ79+4Rp0Chvlq1atnXRRddZL1YIzbMw52PP/64GTNmjLn88stNr1698nAmOrUEUD0DIiACIiACIiACIiACIiACIiACIiACIpDGBN566y3TokUL8+uvv8Z0lUSiNWjQwLz55pumUaNGMfXJjUY///yzQbiNZtOnT7eHhgwZYqZNm2aOPfbYaE3zZD9pBph/48aN8+T8OunfBCSA/s1CayIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQVgSWLVtmPT9jFT/dxSM+IpriFVqxYkW3O0+WhQsXNh999JF37j/++MOQMowcpnfffbf5+uuvzamnnmrWr19vDj30UK+dVkTAEZAA6khoKQIiIAIiIAIiIAIiIAIiIAIiIAIiIAJpRqBLly4GMTMntnnzZnPzzTfbcPic9E9WnwMPPNAcf/zx+w2H6En4+9FHH22LOc2cOdOcd955+7XTDhGQAKpnQAREQAREQAREQAREQAREQAREQAREQATSkMD7779v3nvvvYSu7OWXXzZffPFFRAEyoYGT1Llo0aLmjDPOMK+++qr5+OOPQwRQQvkPOuggU716dbN27Voza9Ys6ylapUqVkLPjUbpixQp7nb/99ps54YQTbDh9tEJQ5Ev95JNPbB8GIhcp+VIPO+ywkHGz29i3b59ZtGiRLX7LPI877jhTo0YNO2fXd9u2bYZQerxgK1Wq5HbbJXNdvny5Xef8CMV+W7NmjfWUrVy5silUqJD/UMatSwDNuFuuCxYBERABERABERABERABERABERABEcgEAhMnTkzKZb700kuBFUC5QIQ+7JRTTrFL9+PEE0805cqVMxR1atWqlfn999/toWHDhlnPVja+/PJLc8UVV1jx0/VjWadOHfP8889bUdK/HzH4sssuM6QW8FuRIkXMww8/bK655hr/7qjr5Fft2bOnJ6K6huQxHTdunKlfv77dxXnIw1qhQgXzzTffuGZ2iaB71lln2fW5c+eGXD/XevLJJ1vv302bNkkADSGnDREQAREQAREQAREQAREQAREQAREQAREQgcATePvttz1BL9pkX3/99WiH4to/efJk6zmZVSdyb5555plZNUn6MUQ+xExEScLgzz777P3OsWPHDtOxY0ebGxTPTooSNW/e3LYjv+npp59udu/ebS699FJzySWXWC9Kiis5EXL27NnmpJNOsu0ZixB7PDI7depkmjZtavbs2WMoMjV+/Hhz7bXXmhIlStiw/P0m4tuBZ+75559v8CRt06aNueCCCwxeqHixwhrBkzmcc845VtQsU6aM+fbbb63o6i/0xDPgjPB/vwA8Z84cmxYAgbRkyZKuWcYu5QGasbdeFy4CIiACIiACIiACIiACIiACIiACIpBfCZD7EuEuFUYoeXa5NcuXL2+LEOXGfH755ZcQcRPhkGunCBLejXg6IvZGCvMmhBxxdNWqVaZYsWK2H2ItY/To0cNuDxgwwNx5553e1PHwrFu3rundu7dtgwiKffjhh1b8JOR+9OjRIe0RGYcPH26eeuqpLAVQRFSEUs7v90RlsKuuusowl/79+xtyty5evNgccsghViAdO3asFVr9Aug777xjhV1YIIDefvvt3pyc+H3xxRd7+zJ5JTQ5QCaT0LWLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgEjgCengh87kVeUzwcET8xRMKffvop6rxvu+02K37SwFWJxwuTsPEjjzzS3HHHHfv1vf76621b8orSDiPnJvbDDz+YLVu22HX3A9Hyq6++Mi+++KLbFXGJZyf5SBEyb7zxxv3a9O3b15Czk8r2U6dOtccRuzE8TZ39+OOPZsGCBdabFfEZkRZvVGdOAHV93f5MXcoDNFPvvK5bBERABERABERABERABERABERABEQg3xLo3r27oYhOVobXYE4rwPvHJQS7bdu2/l37reNdmVtGcaE33ngjZHgESMLCCQOfNm2azVFKDk7C3cPN7zXpjuHViuEdSi7NSEYe0E8//dSGnjdo0MCG+HOd9D3mmGNsGHuzZs1sKPzhhx9uRc1I4/j3IZJihLdHKrJUoEABex7ymvrbFixY0HzwwQdW5ETwfffdd60XKWH/FFAiTytCLd6pCKzkNq1Xr56pWLGi//QZuy4BNGNvvS5cBEQg6AQOOOAA+00m8+SfoEwEREAEREAEREAEREAEREAEHIH77rvPrUZdIhBSxT1RI4x6xIgRiQ6T4/4IheTqjGSEsffr188MGjTIelTi8Yhg67fwqu8cw8MSw3Myu9ylK1eutG2pOP/RRx/ZEHfC75977jn7ovp6w4YNbdh6dkKxEzURXqMZHqCYmyOh/QimeIRyfkRPl/+Tdc6PAIqHLAKo8/5U+PvfhCWA/s1CayIgAiIQKAKlS5e2lQqZFN/2yURABERABERABERABERABEQgHgItWrRImgAaz3lT3bZbt25m8ODB5tdff7Wh8eFh33hMhhs5ODHEz3//+9/hh0O2qSbvDK/QFStWWA9MPE8JS1++fLkVUhFTCc1/5JFHXPP9ls65JSvvXa4Dc21Z55oQQBE+ET3J/0n4Pt6tzpMUr1ByiDIvTAKoxWB/SAD9m4XWREAEREAEREAEREAEREAEREAEREAERCBtCLRq1cp6RxJOnVM7/vjjbYh3Tvunoh8FiBA5KQbkhM3szlu9enXbpEiRIuamm27KrnnIcQTHf/3rX/bFAfjikUthpFGjRpm77rrLEBIfyQidxwhTj2br1q2zh/yerFSKJ0oQwbVDhw42/J+iSRhjEuo+b948mxeVHKl4vVLISfZfAiqCpCdBBERABERABERABERABERABERABERABNKQwMEHH2xD1xHOcmIIfQ899JANsc5J/1T1wRsS8RMjFD0Ww5MTo7I7BYXCbdeuXeaUU06x4yEoYvfcc4+pXbu2GTp0aEhzQtbx+kT0/OOPP8ySJUtCjvs33HmnTJliPVb9x1inmNObb75pd/vD/suWLWvns2jRIjNhwgR7HE9QZ4TI41VKNXuKIcn705H571ICaCgPbYmACIiACIiACIiACIiACIiACIiACIhA2hAgdBpRLCc2fPhwm1MyJ31zuw+enhRCGjdunLnyyivt6RAEEQpjMXJlNmnSxGzfvt106tRpPzGSyvF4VCJmnnTSSXZIPE0pgIS359atW0NOM3/+fCukkq8T4TSacT8Iqd+wYYPp3bu3ocK9MwTMnj172rERWhE1/XbhhRdaD9dhw4bZ3eECKDuffPJJe0wCqMXg/VAIvIdCKyIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQfgT69+9vihcvvp/gFu1K8RwlnPvaa6+N1iSl+6lk78+HyckRDv3h7oSAjx8/Pq55jRw50gq8r7zyiq0ij8DIeci1SRV1igshKFL8CENohcvnn39uKlSoYAVKxNGPP/7YepLSpk+fPlnWcGBMwuQJoWdJ5XYqyeM5SqV7PDzJ60k+z/BrRjzt27evgQdtyAHqDDEUT1+4UE+iUaNG7pCWfxGQB6geAxEQAREQAREQAREQAREQAREQAREQARFIcwJ4FiLcnX/++VYoi3S5hLy3bNnSLF68ODDip5vnb7/9ZvwvCsVWq1bNenEiZFJd3Z8z0/XLaomXJULnpZdeasi7iWclYe7sw0uTauqtW7f2hsC7kwJD11xzjdm7d68hjJ18n+TlxPP0mWeeMYjN2VmDBg0s46ZNm1rBc8iQIfa8eJW2a9fOVnOP5Mlaq1YtU7VqVTu83/uTHVy7y/lJUSdXGCm7uWTK8QP+Usv/W/YqU65Y1ykCIiAC+YQA/1AJ6cD45843trJgESBchjdh0RKcB2u2mTWbnTt3mh07dtiLLlasmOHNqixYBDZv3mwOPfRQz6MiWLPL7Nnw4YvcYRgeJAcdpKCxID0RePbw+1OiRAn7OxSkuWkuxmzcuNF6pOHhFUm8EKO8JUBlbd6/cW+4R5lsPKvTp083q1evtp85+HuPmIgnYqa+t+V9/ddff22fEXJ6litXLstH5JdffrGi6bZt2wzCJH+Xc2L8z0W8LVy4sL0HORlDfbInoHcz2TNSCxEQAREQAREQAREQAREQAREQAREQARFIGwKIe3gxyv4mwBd+NWvW/HtHNmuHHXaYFT6zaZbtYarXn3DCCdm2U4PECGT2Vx6JsVNvERABERABERABERABERABERABERABERABERCBgBOQABrwG6TpiYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI5JyABNCcs1NPERABERABERABERABERABERABERABERABERCBgBNQDtCA3yBNTwREIHMJ/PTTT+b999+3ACpVqmQaNWqUuTB05SIgAiIgAiIgAiIgAiIgAiIgAiKQQwISQHMITt1EQAREILcJUA3wu+++s6cpUqRIbp9O44uACIiACIiACIiACIiACIiACIhAWhJQCHxa3lZdlAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIAAQkgOo5EAEREAEREAEREAEREAEREAEREAEREAEREAERSFsCEkDT9tbqwkRABERABERABERABERABERABERABERABERABCSA6hkQAREQAREQAREQAREQAREQAREQAREQAREQARFIWwISQNP21urCREAEREAEREAEREAEREAEREAEREAEREAEREAEJIDqGRABERABERABERABERABERABERABERABERABEUhbAhJA0/bW6sJEQAREQAREQAREQAREQAREQAREQAREQAREQAQkgOoZEAEREAEREAEREAEREAEREAEREAEREAEREAERSFsCEkDT9tbqwkRABERABERABERABERABERABERABERABERABA4SAhEQAREQgWASOOSQQ8xRRx1lJ1eqVKlgTlKzEgEREAEREAEREAEREAEREAEREIGAE5AAGvAbpOmJgAhkLoFixYqZJk2aWAAFCxbMXBC6chEQAREQAREQAREQAREQAREQARFIgIBC4BOAp64iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIALBJiABNNj3R7MTAREQAREQAREQAREQAREQAREQAREQAREQARFIgIAE0ATgqasIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECwCUgADfb90exEQAREQAREQAREQAREQAREQAREQAREQAREQAQSICABNAF46ioCIiACIiACIiACIiACIiACIiACIiACIiACIhBsAhJAg31/NDsREAEREAEREAEREAEREAEREAEREAEREAEREIEECEgATQCeuoqACIiACIiACIiACIiACIiACIiACIiACIiACASbgATQYN8fzU4EREAEREAEREAEREAEREAEREAEREAEREAERCABAhJAE4CnriIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAsEmIAE02PdHsxMBERABERABERABERABERABERABERABERABEUiAwEEJ9FVXERABERCBXCTwww8/mEmTJtkz1KhRwzRr1iwXz6ahRUAEREAEREAEREAEREAEREAERCA9CUgATc/7qqsSARFIAwJ//vmn2bt3r72S3377LQ2uSJcgAiIgAiIgAiIgAiIgAiIgAiIgAqknoBD41DPXGUVABERABERABERABERABERABERABERABERABFJEQAJoikDrNCIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAqknIAE09cx1RhEQAREQAREQAREQAREQAREQAREQAREQAREQgRQRkACaItA6jQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQOoJSABNPXOdUQREQAREQAREQAREQAREQAREQAREQAREQAREIEUEJICmCLROIwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIikHoCEkBTz1xnFAEREAEREAEREAEREAEREAEREAEREAEREAERSBEBCaApAq3TiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIpJ6ABNDUM9cZRUAEREAEREAEREAEREAEREAEREAEREAEREAEUkTgoBSdR6cRAREQARGIk0DhwoVNvXr1bK9y5crF2VvNRUAEREAEREAEREAEREAEREAEREAEICABVM+BCIiACASUQKFChUytWrXs7AoWLBjQWWpaIiACIiACIiACIiACIiACIiACIhBsAgqBD/b90exEQAREQAREQAREQAREQAREQAREQAREQAREQAQSICABNAF46ioCIiACIiACIiACIiACIiACIiACIiACIiACIhBsAhJAg31/NDsREAEREAEREAEREAEREAEREAEREAEREAEREIEECEgATQCeuoqACIiACIiACIiACIiACIiACIiACIiACIiACASbgATQYN8fzU4EREAEREAEREAEREAEREAEREAEREAEREAERCABAhJAE4CnriIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAsEmIAE02PdHsxMBERABERABERABERABERABERABERABERABEUiAgATQBOCpqwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQLAJSAAN9v3R7ERABERABERABERABERABERABERABERABERABBIgcFACfW3XTZs2mY4dO4YMc95555kbbrghZF92GxMmTDC8nJ1wwglm4MCBbjNPlnPnzjVDhgyx577yyitN69atkz6PyZMnm6eeesqOe/PNN5smTZok9Ry//PKL6dq1q9m6das57bTTTJ8+fZI6fl4P1rJlS7N3794cT6N58+amc+fOOe6vjskn0KFDB7N582ZTuHBhM378+OSfIB+N+Pvvv5vt27fbGf/555+mePHi+Wj2mqoIiIAIiIAIiIAIiIAIiIAIiIAIBINAwgIoH8q3bdsWcjXTp083119/vTnwwNgdTN94442QcXbs2BEyZl5s7Nu3z5vTr7/+mitT2LNnj3eORIS8aJMbMWKEWbVqlT28c+fOaM3y7X6evUS4IRDLgkXgp59+sr8T/P5lum3ZssW8/vrrFkPNmjVN+fLlMx2Jrl8EREAEREAEREAEREAEREAEREAE4iaQsAAa6Yx8aF+8eLE5/vjjIx3eb9/q1avN2rVr99uvHYkRmDlzpnnrrbcSGyQf9a5WrZr5xz/+EdeMy5YtG1d7NRYBERABERABERABERABERABERABERABEchfBJIqgB566KFm9+7dlgDiW6wC6Lvvvpu/qOWD2RJC/MADD+SDmSZvio888ogNm07eiBopLwj06tXL/h056KCk/nnKi0vROUVABERABERABERABERABERABERABAJAIPYY9RgmW6lSJS9E84MPPjB//PFHDL2McQJo3bp1Y2qvRlkTIC0BuUtJI1CwYMGsG6foaLt27Uzbtm3NQw89lKIz6jT5lUCDBg1sLtzGjRvn10vQvEVABERABERABERABERABERABERABAJEIKkCKNd19tln28sjDH7RokXZXuqyZcvM+vXrbbtzzjkn2/ZqkD2BF1980Xz22WfmgAMOMN27d/c6sJ1X9u233xpeP/74Y15NQecVAREQAREQAREQAREQAREQAREQAREQARHIQAJJjzE966yzzLPPPmtREgZPNfesbMaMGfZwmTJlTJ06dbJqGnLsyy+/NBRbInfounXrbOhz1apVDa8LLrjAHH744SHtI21QPIcx8ED97rvvbNGm4447zpx44omG6uCx2m+//WamTZtmvvrqK7Ny5UqzceNGU7FiRUNOypNPPtmcfvrpsQ6VcDvO/5///MeOg8dlrGkI8BYdPXq0d/4uXbqYww47zNvO1BWeM4rQLF++3GzatMkUKlTIHHXUUYbnpHXr1tkyyulzypcHLn8rVdFLlChhPv30UzN//nyzZMkS880339hnrHr16qZp06bm2GOPzfYWkZ5i8uTJZunSpfb3hgJSeG3zO1OvXj3rdRltEP98KHBWrFgxM3fuXDunzz//3Pz888/2d53fnTPOOMPjghf4+++/bwV52lH0i9+NM8880/z73/+2In34OXl+KYRESg2/gB/eLtF7Ez6etkVABERABERABERABERABERABERABNKTQNIFUEQ/BA4EGsLge/bsGbUaPKHa7733niWL92csVeO3bt1qRo0a5YlD7rYg5uBhiNgyceJEc+ONN1phyB0PXzK/3r17mw0bNoQcQrx8++23zaxZszxv1pAGYRsIsAMHDjRff/11yBGKQPFCcEIU5lxFihQJaZPsDQRd5kL1bO7Dddddt9/1RTsn4tiUKVO8w/TNZAF0165dZvDgwebDDz/0mLCC0Mczghj50ksvmdtvv92cdtppIW3YSPQ55fl09+Pyyy+360888UTIebZv3269rHnGEKwRZKMZX0Y8+uijhtywfkPY5MUYjRo1MrfccospVaqUv4ld98/niiuuME8//bSZNGlSSDu+TODFue677z6bAgOG7ksO15g5wI/f/WHDhu33e//OO+9YxoULF44ogCZ6b9w8tBQBERABERABERABERABERABERABEcgMAkkXQMFGGPxTTz1lRaAvvvjCelRGwolA6ASZWMLff//9d3PbbbdZT0vGQ6AjT2CNGjXMzp07rWfcvHnzbO7LQYMGWW/Mrl277ndqBCwEI7zMsCOPPNKOU758ebNixQoren388cdm4cKF+/X178CjDWET4RFjHqeccoopV66cobL9J598YkVZBCG81RCNEHVyy/DgXLNmjSlQoIDp16+fXebWudJ5XARknhu8aTHyqDZp0sTgbYloP2fOHOuJidds//79DcWXatWq5SFJ1nPqBkTwR5Cnwj3PV+XKle3vzYIFCwypJjgfc8Cbk+PhNnv2bHPXXXd5u5krnpp4Xa9atcrwrJOagOVVV11lxo4da8qWLeu1D18ZPny4fbaZD2PhDcvvMYIm3tA8948//rj9EgQBuXTp0tYTGi9WfqfwQMUQQfGuvfDCC8NPEXU70XsTdWAdEAEREAEREAEREAEREAEREAEREAERSFsCuSKAEt6KAIohiiC2RDLnGYbH6DHHHOMJTpHasu+5557zxE/ERrwdES39hgfo0KFDDV5ieIIirNLWbwhKTvzEew+x0F8s6Oqrr7aefYhD0QyhByHIiZ8Iqm3atAnxZiPcFw+3N99804ZPwySrkN5o54plP6HReCRihChXqVIllm5eG8KNSR3g7JBDDnGr+WaJ6Oy/j9lNnHB2xO9wI4WDEz8JD7/77rtNhQoVvGatWrUyL7zwgvWo5P5zj5988knveLKeUzcg4iei54ABA+zS7ecZpGI6Qij2yiuv7CeA8pzfe++99jiCZefOne1zanf87wdCLl8YIIDi4UqhKopoRTMEzpIlS9pUC36hlJD2Hj162G4wwPj969Onj00dYHf89YMvAhyvV199NS4BNNF74+agpQiIgAiIgAiIgAiIgAiIgAiIgAiIQOYQyBUBFLGGF96I0cLg8Vrzh79nh5xQcyeqItYRYosIE2546v3yyy/mnnvusSG4iFNjxozxcg2SL9Sdt3jx4tYzLlzsO+KII6wgRKhvtEr2r732ms2jyPnPO+88Q5hyuDFu3759rSccHqAvv/yyFXvw1EumIVoRaowhNiPExmuE5996663xdgtp//3335tnnnkmZJ/b4H5jFL3i3oQbnrHdunUL3x3XdiRv36wGID/riBEjQpogWo8fP97uo2gUoqNf/HSNL7vsMoNnJR6NeA3zXHFfk/WcuvOwZB48R/xO+e2ggw6yzykelM7zEg9V2jtDzCQ9BNayZcuIzwb3nueH5517iOCKGEpIfCRDSEUU9ouftOPZq1u3rk39wDah9ITUh4vS11xzjUH4xHvVFUCjfXaW6L3JbnwdFwEREAEREAEREAEREAEREAEREAERSE8CB+bWZblq8ORCJAw+3AgfJ4ch5tqGt/FvEy7rRDQ88CKJn659s2bNrBjFNoWJyA3qjPBlZ+RMDBc/3TEK3ZC7M5rhbeesY8eObjXi0gmSzJ/8hsk2xGDEJFIC3HHHHSECWLLPldV4CG0Ug4r0cv1IPxDpeG5wceeMZ8lzhtCG8VxmJVYjerdo0cLmm3XPUbKeU/+cKVAUrcgR4uXRRx9tm/N8If77jUJFGCJku3bt/IdC1hFTKbbkLKv7QWEtwt4jmZ8Xv4fh4qfr40RlPLXxQI3FEr03sZxDbURABERABERABERABERABERABERABNKPQK54gIIJ8dAVbYkUBu/C38mrSAh8dkYRFmcNGzZ0qxGXeKg1aNDAeuXRgL7uHAivzrKrUI8HnJun68MSoQlPOQxv0ewqzvvzQ1JtPpn2xhtvWC9bxqTgVLhXXjLPFfSx8GA8+OCDY55mePoEOvrzvkZL3eBOwPMR7iWZrOfUnYNlpDB9//GiRYt6m+TIdIYg7cTF2rVr28rt7lik5amnnurtxqM1moWnlPC3ozq8s6zmjRe3M/+c3b5Iy0TvTaQxg76PL3qaN29up+lnG/R5a34iIAIiIAIiIAIiIAIiIAIiIAIiECQCuSaA4uFFXk+qo5OX018NHsHDVdeOpfgRwAgtdobomJ352/hFKTwlnVEEJiuLdnzTpk025Ji+FI/BCzBWS6YAigj74IMP2lMT+o/HXV4aIhv3OpIhiJNO4PTTT7e5WyO1SXTflVdemXCRKTyWneVETE7Wc+rmwJKiWlkZRa+cEQLvzC9i+n8f3PHwJTlREVNJqYDXdHg4vWvvF1zdvkjLrL4YOPDA+J3PE703keYY9H145pJKAIvmTRv0a9D8REAEREAEREAEREAEREAEREAERCCvCeSaAMqFEUKMAIonmr8avKvUTpuswsw57syFsePd6QQBdyzSksrTzgi7dubyIbKd3TjRBFC/iImYiwgaq8WT8zCrMfFCJQ/jr7/+akWrG264wfP28/fzh0QzV+cRCEcEr2Qb40Yyl5cS4Stam0j9Ur3PpWXgvP5nKNZ5JOs59Z+P1AY5MTcX+kZ7lsPH5ZoRQHfv3m0LhZEnN9z83pvhx/zbORE5/f3D1xO9N+HjaVsEREAEREAEREAEREAEREAEREAERCAzCOSqAEo1+NGjR1uSM2fO9KrBv/vuu3YfeQRj9bLD6wyhEeEP0S878Q4RxxkFdpz5++3cudNEEnhcW84VyfwCUM2aNU3Tpk0jNYu4D4+uZBjVvZcsWWKH4lopypOd4XXrPG8J/3/kkUey65Jxx/3ejTwf8VqyntN4zxupfU6uxf97E014TbawGWnukfbl5HoijaN9IiACIpBOBPDW58td/meRw5qoAN7r8CVvkL9wTKd7oGsRAREQAREQAREQAREIPoHkqHFRrpMciwiEFCKiGvxNN91k8EL86KOPbI9Yw99pTFGi1atX234bNmwwVatWtevRftDGmV/k5AOBC1MmlN1/zLV3S7/nqNvH0hVwcfsuvfRSt6plPifAc+bshx9+cKsRl3zoRCT3i9rJek4jnjDOnf5r8f8+RBuGSvLOmxnx0x9aH61PKvf7rycn9yaVc9W5REAERCC3CfA3e+XKlfY9jSve5z8n4ifvwyii5//y199G6yIgAiIgAiIgAiIgAiKQKQRyVQAFIiHuCKAuDB4PMzw4eWNO3spYzS86IoRmJ4CuWrXKG9pfjKVatWrms88+s8fWrFljsiroEi1cvUSJEjbXJN4WnIcPIX4RzDvx/1YIJyYFAHkY8Xh1FcPD28WzjRfqVVddlW0XPEVfe+01245q4eTgxCIVALIHMvyH/1nxpzqIhGXp0qWmS5cuNlQeEZyq8Ml6TiOdL959fsGQZz1aTk83Lr9XtMH8fd3xvF4mem/yev46vwiIgAgkiwDvqUgnxPuLaMYXdKRC4b0METeVKlWK1lT7RUAEREAEREAEREAERCDtCeS6AEoY/KOPPmpB4gXqcnCefPLJBiExVqPAjrPnnnvO5heNFoqLKDl79mzbnMIhnMvZGWecYV588UW7OWHCBBu+7vJTujYsEYJefvll/66QdebzySefmL1795pJkyZlGYKOADly5EjbHx4DBw4MGSsnG3hzdOzYMduuFIByAiiicSx9sh00jRvgKeMMbm3bto3qCTl37lxb2AlPYifOJes5dXNIZInQjuCPhxAfgilQxfMXzZ599lnv0GmnneatB2Ul0XsTlOvQPERABEQgEQJ4wLv/P7GMQwHChQsX2vD46tWrx9JFbURABERABERABERABEQg7QjEX4o5TgR4PNatW9f2QgCdM2eOXadAUjyGIHP88cfbLniqIV5Gsl27dnmV0TlOmL3f47JOnTq2Oj3HGAfxMpJNnjzZ4DUXzSg65ATYsWPHRm2LAPnMM894w7Rs2dJbD9IKXqzLli3zXmwn04YOHWruvfdeQ6X2IBtess2bN7dT5EPm+PHjI073+++/94R0vHHr169v2yXrOY140hzs7Natm9drzJgxJlroOF8YIJBihL6fe+65dj1IPxK9N0G6Fs1FBERABHJCgPc48+fPt1++xdufaJxY0qHEO67ai4AIiIAIiIAIiIAIiEB+IJDrHqBAIAx+8eLFXn7Bgw8+2AvFjgdSz549zXXXXWff+FNcCc+2Vq1aWS83wuopCoS3qat+Xa5cORui7D8H3p633nqruf766+04Dz/8sG3fokULGx5GXzz/XnrpJX+3/dbxrKMPXqKcu0OHDvbVuHFjGz5MeDwFhx5//HFbVZsBCPknDC2Ihmeu3zsUBiVLlkzaVBs2bJi0saINdNddd+Wo4MPtt98e4o3Ms4EYyH194oknDKHwPGdVqlSx4YZ4/vKccRzr1auX8RfGSsZzGu0a491/0kkn2eeO6yEM8tprrzWIovXq1bMFMsiH+/bbb4cIvZ07dw5sioRE7028/NReBERABIJE4Msvv7S51HM6p0WLFpkyZcrk6H9lTs+pfiIgAiIgAiIgAiIgAiIQBAIpEUAR/hAaXX7BBg0amGgVprOCgug4YMAA88ADDxhyW86YMcO+yCcaXrEdb7HBgwfbXJ3hY5L3k3HuvvtuG8KOtycv/ziItAhFw4cPD+/ubXfq1Ml+EJkyZYodZ9SoUYYX+UDDPSgJw+/Xr5/XVyvJJ0A+tJxYePGIww8/3AwZMsQMGjTIbNmyxUyfPt2+/M+HO0/r1q1Ns2bN3KZdJus5DRk0gY3evXvb3z08sPm94fcCC78evhxAIEXsDaolem+Cel2alwiIgAhkR2DHjh2G6INEjJyhfNHLeySZCIiACIiACIiACIiACGQSgVwPgQcmooULX2c7nurvtPcbYioh5eQyJL8n5hc/8Wxo37699bysWLGiv2vIOuMgViLGOnPjUAAGkTU7r0XO36dPHyuS4h3oQuL94ifFhm677TZz//33G0RVWf4ggGDNc0YoeKTnjA+PiOPdu3ePeEHJek4jDh7nzmLFilkx98477wzx7HTPO89to0aNbJ5aBNCgW6L3JujXp/mJgAiIQCQCyQpfT1REjTQ37RMBERABERABERABERCBoBM44C+vzP+WfQ76TCPMj6nzgYCiR3izEfKOMOWEyAhdIu7avHmzIVcnnoC1atUKCYeO2CHKTvozDiHTxYsXt2JT6dKl455PlOG1O48I8JzxgZHnDM9lKuki6sdqyXpOYz1fdu3IIUf+W557roPK9fEUJMtu/FQeT/TepHKuOTkXHrtffPGF7cqXO/5CUDkZL5368Axz//PaSIVBkZmcRDXk9dzT/fz8T3Ye/qQpye9fQq5bt87gBZqo8R6J9zpBMNIFETXjTyMThHlpDsbwXsF9oV+4cGG9lw3YQ8H/HX5/KIrK75AsWAT4W817FKKrihQpEqzJBWA2OGfE81kq2VPmvdv27dsNtUri1Q2SPReNJwIikFoC+VoATS0qnU0EREAEUkuAL1NcwbeaNWuaCy64ILUTCPDZpk6dmqNCMAG+JE1NBERABERABERABNKeAA5L/ujQVF+wBNBUE9f5RCA4BFISAh+cy9VMREAEREAEREAEREAEREAEREAEREAEREAEREAEMomAYiYy6W7rWkVABEQgTQjUrVs3MCHw5NMlRFQWLAKEv1P0ByOXc34PgV+7dq35+eefE4ZMuF+dOnUSHicZAxAmWqBAAYXAJwNmkscgBH7fvn12VEJ4FSaaZMAJDkd4NX8PSL+iEPgEYeZCd+4N94jfG4XA7w+4aNGi++/UHhEQARFIAQEJoCmArFOIgAiIgAgklwDhU0EwckiRJy8vc1kFgUMQ50B+PJczk3xj5MrLz8ZztnTp0oQvgWe1cuXKCY+TjAHIRU3+T30YTgbN5I6xdetWL4cu+ewlsiWXb6Kj8cUbvz/kcFcO3URpJr//xo0bPQGUPJMyERABERCBYBBQCHww7oNmIQIiIAIiIAIiIAJRCRxxxBFRj8VzoHz58vE0V1sREAEREAEREAEREAERSAsCEkDT4jbqIkRABERABERABNKZAGkWjjzyyIQukVQARx11VEJjqLMIiIAIiIAIiIAIiIAI5EcCEkDz413TnEVABERABERABDKOQO3atRPKZXrccceZf/zjHxnHTRcsAiIgAiIgAiIgAiIgAhJA9QyIgAiIgAiIgAiIQD4ggAfnP//5zxyJmIin5cqVywdXqSmKgAiIgAiIgAiIgAiIQPIJSABNPlONKAIiIAIiIAIiIAK5QoAiRo0bN7aV7WM5AR6f9erVM9WqVYuludqIgAiIgAiIgAiIgAiIQFoSUBX4tLytuigREAEREAEREIF0JVC8eHFz9tlnm1WrVpk1a9aY3bt373epVO0mZ2iNGjViFkv3G0Q7REAEREAEREAEREAERCBNCEgATZMbmYzLWLx4senataspUqSImTZtWsxDLl++3MyaNcssW7bMsI5VqlTJkGusbdu2hsIN2dn27dvNSy+9ZPuvXLnS7N2711CpFq+VVq1amdKlS2c3hI4nQGDDhg0m0QrDHTp0MJs3b7b3e/z48QnMRl39BA488L+O+m7pP6Z1ERCBzCWAZ2f16tXt66effjI7d+40e/bsMQifhQoVMiVLljT6u5G5z4euXAREQAREQAREQAREIJSABNBQHhm7tWPHDjN06FDz559/xsVg0qRJZuTIkeb3338P6Yeg+cUXX5ipU6ea22+/3TRq1CjkuH9j9uzZ5t577zXbtm3z77ZiKIIqwminTp3MZZddFnJcG4kT+PXXX824cePM9OnTzauvvprQgHwA5x7u27cvoXHU+W8CZcqUsV8isIfcfzIREAERiESgWLFihpdMBERABERABERABERABEQgMgEJoJG5ZNTeX375xdx8883m22+/jfm6EUoHDx5s3nrrLdsHj5OTTz7ZnHrqqWbXrl3mww8/NF9++aVBCO3fv7954oknTMWKFfcbf8aMGWbAgAHe/qpVq9ox8P7EE5TjjPHoo49aca1z585eW60kTqBbt25mxYoVpmjRookPphFEQAREQAREQAREQAREQAREQAREQAREIIAEJIAG8KakckpLliwx99xzj1m3bl1cp33jjTc88bNChQrmoYceCglTx1tz9OjRZsKECQYvQzw8ETH9htfpww8/7O1q2bKlDcFHTHV2xRVXmDvuuMMsXbrUEFbdoEEDc+KJJ7rDWiZI4Oeff7YjHHDAAQmOZEyvXr1sHjr//Ut4UA0gAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgkSUBX4BAHm1+6IkoiWXbp0iVv8JD/nY489Zi/9kEMOMSNGjAgRPzlA3jHC1hFHMfKLhoe4jx071tuH9+iNN95oc5fZDv/7QbVbPEQPPvhgu4dzyYJJAHG6SZMmtjpxMGeoWYmACIiACIiACIiACIiACIiACIiACGQiAQmgGXjXCU2/+uqrDfk7CWXH+69du3a2YEIsOObOnWvI94i1adPGlC1bNmI3CjQ0b97cHHXUUaZWrVr7hdiTI9RZjx493Op+y3Llypnzzz/f7qfabbzeqvsNqB0iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIZQ+DvWOOMuWRd6Mcff2yo+o2VKlXK3HnnnbbaOoVwsOzCod977z3bjh/nnXeetx5pBWGVV7j98ccfnpBJ1fnKlSuHNwnZrlatmrf96aef2irz3o44VxCAX3/9dVtkadOmTbZaLiItVetbt25tDjvssCxHpD+s1q5da6+BKvfkLuV1wQUXGLxWoxlet3jQlihRwlA1PZqR/3Ty5Mn2MAWkyK3qN9ILkEIAYRlxmDyp7777riGlAS9XHbhu3brm4osvNgUKFPB3N4888ogNV3dCNh7B999/v22D164rOPX555/bPKwcuOGGGwz5Yp977jkzZ84cc+ihh5r69evbtojg//nPf6wwzv7u3buHnM+/8dtvv5lp06aZr776yuZ53bhxo80Pyz3GE/j000/3N99vnYJb77//vr3e9evXmx9++MFQLIhn6OijjzbNmjWLKsrvN5h2iIAIiIAIiIAIiIAIiIAIiIAIiIAIpD0BCaBpf4sjXyBFb/DevPTSS7MV/MJHoDI7huh05JFHhh+OaXvz5s1WCKRxpOJI4YMcccQR3i4EPuYdr1GcicJNFGjyG3kwEeEQVqk4T9X60047zd/Erm/dutWMGjXKy33qGhDaTwEpRLmJEyfaUP6mTZu6wyFLhD/ERrhlJYAiUE+ZMsX2RaQOF0DffvttK/zt3r3bitcUsfruu+9CzoU4iFhN24EDBxo/Q3K47ty502uPKOvOd8IJJ3gCKB63bn+LFi3MbbfdZhCNnX3zzTcG4RIR9p133rEcEYSjCaCIxszl66+/dkPYJSkSeCH6nnXWWaZ3794GYTzcEHoZm3H8hhi8atUqu+uFF16wRb3OPfdcfxOti4AIiIAIiIAIiIAIiIAIiIAIiIAIZCgBCaAZeOMRmCguVKhQobivfs+ePZ7Qdswxx9j+eHMiSi5cuNAQ1o7QWKNGDVOnTh0rZpEPNNz8VcfxKszOtmzZ4jVxXovejhhW9u3bZwss4VmJFSxY0OarrF69uk0DgEfj/PnzrVclVevxkMS70hleh4h/eC1ieIk2btzYXidCItc/b94823/QoEHWs7Fr166ue64tET07d+5s4IMX6/HHH2+KFStmFi1aZOfKvJctW2a4pjFjxnjzwHOXe4k4iiBLjlXSFWAub6vX+H8rFLFy4if3lPtODtgzzzwzvGnEbbxJETYRWzGekVNOOcWQ4mD16tXmk08+sULyzJkzDV62Tz/9tEFMdYbnKPfAiZ94tyIMFy9e3CDU0p9j3A9EVjxRIwnZbjwtRUAEREAEREAEREAEREAEREAEREAEMoOABNDMuM8hV0modk4NT0nyhmIIbYhSCH6EX/sNQfDll182U6dOtVXc8Rb1G+Jr6dKlrRcj3pOIYq7Qkb+dW/fn/fR7Lrrj2S2fffZZK0rSjuu/++67Q4S+Vq1aGTwHEfmYy7Bhw8yTTz7pDUvYtxM/Ee4Q2MqXL+8dZwUP0KFDh1oBGE/Qc845x4p8IY2SvLF06VI74vXXX28uv/zykCJSCIK33nqrQQSlHZ67zB1zHpq0QQBFEEaczMrwkOWaKVZFqDpiMs9DLEI6z8nw4cM98ZPiW3gg+8VxBFm4v/nmm1Zofeqpp7x5Mi+EXIRRDA/gnj172nX3o1u3bub55583pAfA8LaVAOroaCkCIiACIiACIiACIiACIiACIiACmUtAAmjm3vscXTnenc4QLPHIQ0Qj5yT5F/FCxCsRTzyEtwULFpj27dubcePG2Xyjri/LKlWqWAGUdhMmTLCFmfzH3TrhzQipztiOxxDWxo8fb7uQ35Sq8pG8HMl7OXv2bOvJumLFCpvfs1KlSvZaEOMwvArvu+++iAWjqICON+s999xjvSMR8/C6zC6nqh04gR/kCI2UZxXvSifsMjzFq5wAmpPTcR133HGHzZVKfzxk/V6yWY352muvWY60wfsUsTbc8Cbt27evIaweoRMB/cILL/TyveLV6uyMM85wqyHLli1bmldeecUKrQjl2QnrIZ3DNnhuSG+Ql+a+bGAOCNWkPJAFi4C7R3wZIAsWAXdvmBWRA6Q7kQWLAPeI/5v+9xbBmmHmzsb/+0Ou7dx+L5O5pBO7clIDyYJHwP3+EC2l9wfBuz9uRqRkI5KMz3cyERCBzCAgATQz7nPSrtL/IQVREvGSUPh+/fqFFDIipJmwa0KT+eD58MMPW+HRPxFEO8RT7JlnnrG5JMNzXSL6IDj6w955MxGP4bmImIWdffbZnqAWaQyEOYrpkJcUQQ6jP9eJISiWLFnSrkf6QQEexFw8VvEYxbs1lhynkcaKdR/pDKIZhZ3wbMX8DKO1z2o/4inj5cQQJZ117NjRrUZc4hlKYS6Yk1fU5Ur1F5dCUK1du/Z+XsPcM7xvEeQTNd68ujewiY6VrP5Bm0+yrisdxtG9Cf5d1D0K7j3SvQnuvXEz0z1yJIK11H0J1v2INBvdo0hUgrGPe6P7E4x7oVmIQKoISABNFek0OQ+5NJ0hUCFKjRw5cr8waLw7ET0RFPHEI68jRXQosOOMfJV4A1KQB089vEnJJ3nSSSdZb1GK2lBtHY9S9hHCzVj+vJBurKyW5CZ1duKJJ7rViEu8KXn5DY9EZw0bNnSrEZcIbw0aNPAq3NM3twXQrApR+XOtEoaeiOHdmxPjOfn+++9tVwox+YXMSOP5vUr9hZ3++c9/es1JuYCXLoI2nq41a9b0RM9kiJ+cCG+XvPZ4wWONvLpY2bJlDTlrZcEi4N445/WzEiwqwZiNuzduNrpHjkRwlu4e6d4E5564mbh747Z1jxyJ4Cy5R7ovwbkf/pn4f390j/xkgrPufn90f4JzTzQTEUgFAQmgqaCcRucIz/dIAZ7wfe5yCSm48sorvZyMCDl+AZR2PXr0sKFv5M/EqFrOy294hd51111ekR4KEMVjVG93hogUr639K5zfmb+SutsXvvS38Yun4e2SsV2gQIEsPVIPOujvX/F4PWfD55dTAZTCSU58/fHHH60QHj52tG2/AFqiRAlb3f3BBx+0KQbwrqVQEi8qxtevX9+Kz+T9jPcZiXR+vEkp0JSXBjf3/JGnNa/nk5csgnpuwg+5T9kJ+0GdfzrPiy/MXMoUclZH+1+VXMGVcQAAQABJREFUzgyCfm2EHxJ66P+yLuhzzpT58d7JRc+Qs93/fiJTGAT5OvlyWeG7wb1DrmYCue5z8tkjuFeWHjMjwpD3b9So8NcjSI+r01WIgAhkReBvdSSrVjomAv8jEP4BEi/OrIxK3c6ckOO2WSJUUZAIL1BCl2nDmzqMUPOLLrrIXHXVVbbKudsfrweoPz8Sb+LjNYQ2DM/CUqVKZdvdf47czvvD/UjVN5f+68oWgq+BX8TEgxgRNFZbv359SFO8iHkjidexuy80QOTAK5QXuWkpktSpUyfPKzRkEG2IgAiIgAiIgAiIgAiIgAiIgAiIgAhkFAEJoBl1uxO/WL8IhiAYXt09/Ax+r0G/EBbejlB4Xngb4DWJNwjnct/K+YVE/5jh40Ta9nuW4BEUr9Ef0Q4Blm8Mw0Xg8PH8hTaiibX+0Jjw/mw7sTfSsbza5+5FvOf3JxYnVL1p06YxDxHJ48SlKaCyPYWdyCNLvlXHlHQK5GElZcK9996rxOYx01ZDERABERABERABERABERABERABEUhPAhJA0/O+5tpVEUaIF+SWLVusSIeHXlaCpAs/ZEKEKWdnhB1TVCncli1b5u2KtxCPf35UMs3KENEQH/3CG/0p6oRt2LDBVK1aNashbBvXgDQAkcyFhEc6xj6q4qaLVahQIeRS8M5Mhrkq9O3bt7fVnefPn29mzJhhPvroIzv8ggULDPsIiZeJgAiIgAiIgAiIgAiIgAiIgAiIgAhkLoEDM/fSdeU5JeAvUrNkyZIsh/F7ffpzY9Jp0aJFZvLkyWbs2LHGX1zp/9m7D3Apiuzv4wcJkpMgQckoIiZMoBgQ4cHArlnMrwFEEFlcUDGtwoqoKIoBUUwoKyuLIijmuCysYsKsgGIgCZKDBMPrr/5bbd9hZu7MTRP6W88zTE93dXX1p+8F7rmnquI1+Oyzzwa7w8Pqg51JNsKLBIX7E+8UZRVqYZ2TTz7ZZRGqTjiA5wOh8c71+7R4ky/ha2ufD6wqSzFZ8YsGJauTK8c0d6fPhJVNYcHfTZs2uaxOZQL7+cd0r9qvhY9mz569za0rS1fPbcSIEXbRRRcFx5UdSkEAAQQQQAABBBBAAAEEEEAAgWgLEACN9vMv0t137do1OE9zLvqhx8HO0Mb06dODT7GZeBq+PGrUKHv44YctvFJ7cML/Nj799FM3N6g+asVvZaGmU3bbbbeg+tSpU5MGW9UnLRakhXt88LJdu3bB+RMmTHDHgx0xGwrwzZw50+3VojX7779/gRp++LzmJV2zZk2BY/6DAoSzZs3yH0v13Q9rL+4CSYV10hsq8Dt58uSk1fWMBg8ebGeeeaYNHz48qHvuuefaBRdcYJdffnnSDFl9jfiiBQIoCCCAAAIIIIAAAggggAACCCAQbQECoNF+/kW6e63K7lc0VMDwvvvui9uOAoEzZsxwxzRX6AEHHFCgXjhQlSiwqEDksGHDgvN69+4dbKe60bx582AFeQ2Bf/zxx+OeqqzLJ554wh3TvJVaVVxFgVu/2JMyQDW/ZLyyceNG0wrlvihQrCH94RIe3q+FfOIVzVs5f/78eIdKfJ+CtCrqezjbsqQvpKxMH2xVxu+CBQviXkJZn48++mhwTJm4vnTu3NltaoqCcB1/3L+Hg8dhb3+cdwQQQAABBBBAAAEEEEAAAQQQiJYAAdBoPe8SuVsF9ZSF5wNa//jHP+y2226zDz74wA1vVtBSAaqrrroquN4VV1yxzYrcGsq+zz77uDqar/H66693K3sro1RtaGX4Sy65xPxwcK0A3qZNm6DNdDYuvPBC88G+Bx54wGUWaji1si21MJIyWfv37+8WOVK7gwYNKrB4zsCBA4P7HTt2rA0dOtQ0XF4ZjcrkVLC3V69eQSZrw4YNrV+/ftt0Uava+/LCCy/Yrbfe6oZ76x6VLSsn7U9ltXnfTnHeNTxdRUFFXVsB4HDWbnHaDp/bunVr0/NT0UJSslIgWSu563lrrlhdd8CAAW4+T9VTwDM832uPHj2CZ6ggtgLFCqTqGfqsXT1bBVhV9Lz9Nd0O/kAAAQQQQAABBBBAAAEEEEAAgUgKsAhSJB978W9a2ZG33367C1quWrXKnn76affSHJfhOR7LlSvnAos+mzJ8ZQVQr7vuOjeseeXKlfb666+7V8WKFbcZpq4V4i+99NLw6Wlt16tXz2688Ua74YYb3AJOCjLqpZXsY1dcP/XUU+2oo44q0L4CeAp6KmCpgKcW29Er3vnKONXQbT/vZbghZb1qFXOfpajh3nqFi+Yc1XV69uwZ3l0q28pufffdd13b7733numlhZuOPfbYEr9enz593HOdNm2aCxyPGTPG9Ir9mtGFNXXAtddeW6APWoxKXy9XXnmlC5pqXli99DWmr6Xwc9R8s3reeu4UBBBAAAEEEEAAAQQQQAABBBCItgAZoNF+/sW6+3333ddl2x1yyCFBZl44+KkMT83xqYBioqIA1fjx4+34448PMizDCyK1bNnSDYEfMmSIC3QlaieV/QqqKTO1W7duQX/DQTMFLtVfZZ3GK8pI1PlHHHFE3PM1zF8rko8bN86aNm0arwl3DzfddJPLDo2dy1Tzg8rynnvuCaYYiNtICe488cQT7ZxzzjGfCaqmk81PWpxLKyPzsssuc8Z6rj6DOPw107hxY9OzHjlypFWqVGmby2n6BfmG55NVBql/jnXr1nWLIamOgtYUBBBAIAoCmj5Ev4i899573b/LL774YpBNH4X75x4RQAABBBBAAAEEEChMoNzvwYPfCqvEcQQKE9CXkYYza+5KBfIUAFQwK52yYcMG0w9xixYtMq3qrYw/ZfIpw6+ki/qrYedatKhatWrWrFmztLIFdf6SJUvc+coC1ZB3BVB9UC/V/i5dutTmzZvnzm/VqlXa56d6nVTqacEgzQWq+V39dAGpnFfUOppzVM974cKFLutUXy/169dP2UDD5uWn6RL0DDTfZ75lfOqZKJCh0qJFCxcgL6o355WOgH5hoCB+vn3tlY5W2baq6U3094SKfuHkF6Er216U3tX079CkSZPslltuMU0jE1v0S6Tu3bu70Qvt27ePPZwVn/V3nObc1r/5lOwS0MgcPze4/m3WaA1K9gjoF7/6/tEvsPU9RMkuAf3/VH9H6+cCv25CdvUw2r3RdFz6/5ueTbo/u0VbjrtHIPcFCIDm/jPkDhBAIE8FNMfsihUr3N0pKK3pCSjZJUAANLueR7g3+RwA/fHHH+2UU06xN954I3zLcbf1S0TNa615k7PtBz0CoHEfWVbsJACaFY8hYScIgCakyYoDBECz4jEk7AQB0IQ0HEAg7wUYAp/3j5gbRAABBBBAAIF8EdAP1ppXO5Xgp+5ZWUiaV1rT0WjBOAoCCCCAAAIIIIAAAlEUIAAaxafOPSOAAAIIIIBAzglouoUTTjjBFixYkHbfn3zySfvb3/6W9nmcgAACCCCAAAIIIIBAPggQAM2Hp8g9IIAAAggggEDeC9x///321ltvFfk+NQx+7ty5RT6fExFAAAEEEEAAAQQQyFUBAqC5+uToNwIIIIAAAghERkDD14cPH16s+1UG6YgRI4rVBicjgAACCCCAAAIIIJCLAgRAc/Gp0WcEEEAAAQQQiJSAMj8XL15c7HueOnWqKRBKQQABBBBAAAEEEEAgSgIVonSz3CsCCCCAQH4IaD5DrYSd6bJx40a3sEz16tUz3RWuHyOwadMm27x5s9tbpUoVq1SpUkyN3Pr40ksvlUiHV61aZcOGDbOddtqpRNorTiNr1661ihUrmp4PJbsENmzYEATK9fdb+fLls6uDEe+NMsLXrVtnVatWdd9DEefIuttfs2aN61O5cuWsZs2aWde/THeoVq1adtppp2W6G1wfAQQiKFDu99VBf4vgfXPLCCCAQNYLbNmyxVasWOH6qQBB7dq1s77PZdXB9u3b25w5c8rqclwHAQQQQAABBBBAoAQEWrdubfPmzSuBlorWxE8//WSrV6+2Bg0a2HbbMSC2aIqchUBuCvAdn5vPjV4jgAACCCCAAAIIIIAAAggggAACCCCAQAoCDIFPAYkqCCCAAALZJTB48GBbtmxZxjulLIJffvnFGAKf8UexTQc0/N0Pga9cuXLOD4F/7bXX7Nlnn93mPouyQ98/jRs3LsqpJXqOhvBqCLyeDyW7BDQEXn+3qVSrVo0h8Nn1eNzUK+vXr3fTR+h7iJJdApreQ0VD4GvUqJFdncuC3jCiKQseAl1AIKICDIGP6IPnthFAIPsFGAKf/c9IQ6i0oEy9evWyv7MR66GCAwqwqWi+Mc2Vl8tlxowZdthhhxX7FmSxfPnyrJg3UL/EUPCTOfKK/VhLvIGVK1cGv0CoX7++VahAzkSJIxejQQWn9f1Tp04dfoFQDMfSOnXp0qWmWeY0vFrDrCnZJcAQ+Ox6HvQGgbIU4H8zZanNtYot8OWXX5p+CPziiy9M2yrNmjWzvfbay84444wiZ2E99dRT9sgjj7j2xowZYzvvvLPb5g8EMimgRX6mTJniurDrrrtat27dMtkdro0AAhkUOPjgg90P0j/88EOxevGnP/0pK4KfxboJTkYAAQQQQAABBBBAIE0BAqBpglE9cwKTJ0+2u+++OxiS5XuiDKwPP/zQnnnmGbvyyitNPySmUxYsWGD33HOPKdtOxQ/5SqcN6iJQGgJa5VWrjKv4obylcR3aRACB7BfQKtz6N27gwIFF7qxvo8gNcCICCCCAAAIIIIAAAjkqwCJIOfrgotRtDSG54YYbbPTo0S44qWFYHTt2tEGDBlnfvn2tXbt2jkOB0Ouvv96+++67lHm2bt1qw4YNC4KfKZ9IRQQQQAABBMpYQP/m7bvvvkW+6qWXXmq77757kc/nRAQQQAABBBBAAAEEclWAAGiuPrkI9fu5556zF1980d1xkyZNbNKkSTZy5Eg7/vjj3bB3DVk//fTT3XHN6XLzzTenrDNu3DibP39+yvWpiAACCCCAQKYEKlWqZFOnTi3SNC3HHnus3XTTTZnqOtdFAAEEEEAAAQQQQCCjAgRAM8rPxQsT0LD0e++911Xbfvvt7fbbbzdNxh8ummC8T58+puCoyscff2yrVq0KV4m7/cEHH9g///lPd6xKlSpx67ATAQQQQACBbBLQHNWzZ892IyFS7Ve/fv3s6aefZiXvVMGohwACCCCAAAIIIJB3AgRA8+6R5tcNvfXWW7ZmzRp3Uz179ky4kqLmNTv66KNdVoyG933//fdJIbQysIbVa3j9gQceaAcddFBQv1y5csE2GwgggAACCGSbQKNGjWzmzJn20EMP2W677Ra3e/rl4JFHHunqaZ5rVvGOy8ROBBBAAAEEEEAAgYgIsAhSRB50rt7m66+/HnT9mGOOCbbjbZx99tmmVyrltttus2XLllmNGjXcohJaXKm4Zfz48a5NtXPxxRdb1apV4zapuUz9gkvKyqlWrdo29RSYvfPOO109Zfv4If7hip9++qm98MIL9s0339i3335r1atXt1atWrlXjx49rF69euHqBbbHjh1rCgLvscceLnC8fPlye/PNN01ZsWpX19Q8c5prNTxf3MKFC129999/37788kvbcccdrXXr1nbuueda48aNC1wj9sPPP/9s06dPt88//9xNO7B06VJr2rSpO3///fe3ww47LPaU4PNHH30UTIPQq1cvq1Onjr377rv2zjvv2CeffOLmfVVbWim9e/fuCQMCQYNsIIAAAjkuoADneeed515ffPGF6e/lJUuWmEZL7LTTTnbooYcm/Xcgx2+f7iOAAAIIIIAAAgggkJYAAdC0uKhc1gIKsqko0KYf6EqivPTSS/bqq6+6pgYPHlxiPyBq2P20adNcu1qJvlOnTtt09+uvvzatZu9Lonpz584N6p1zzjm+untfuXKlad5TPy+qP6jrK/P1jTfecPOk/uUvf3HBQH88/C4DBT21CJSyh7QwxooVK4Iq2v7www/t0UcftTvuuMP22WcfF2iU14YNG4J6ys6dN2+evfbaay6jVgHTeEVBWi02pbrhoukK9JoyZYp16dLF1L6C0rFFC1t5WwWDtf3AAw8UqKZFsBQoVVsKLJ966qkFjvMBAQQQyFcB/T2eKBM0X++Z+0IAAQQQQAABBBBAIB0BAqDpaFG3TAU2b95syjhU2WWXXdz7r7/+6gJxc+bMcQG6jRs3Wps2bVwmowJoyohJVpR1OGrUKFelW7duLuiWrH46xw455BB78skn3Smany1eAFQZi+GijJ149WbNmhVUUxaPL7/88osNGTLEZVFqn7JHdV0ZrF+/3tno2n6IvxZ4UjZqorJgwQLr37+/rV271nbYYQfr0KGDC0Aqu/Krr74yXe+6666zSy65xG655RbTIlPK1lSmpTJoVU+BRz0ruU6YMMG0SEe4KKtUgU2f9aq+6joNGzY0BYTffvttF7hVEFXZp4888ojLZg23Ed5W8HfGjBluLju106JFC9cXWSpwqz7fdddd1qxZM3ed8LlsI4AAAggggAACCCCAAAIIIIBA9AQIgEbvmefMHStYqaHgKrVq1TINoda8nT5709+IhkAr8PjMM8/Y1Vdf7bJF/bHwu4KnOl8ZjMooVdZjSRZlSSogqfYVhIxXYgOg7733Xrxq5gOgWvBJAUNfFGDUEHIV7VdWZezQc2WAjhgxwhQcnjRpknXt2rVAG74tvWvYpIqyTHv37u229YesL7/8cjfEXBmnQ4cOdUFJZYPut99+Qb0ff/zRnad3Db3UfSsg64vaUWDUBz+Vmam5XMOBagVPNSXB888/bz/88IM9/PDDLuDq24h9V/BTQU/1Se++6FqDBg1yw0C176mnniIA6nF4RwABBBBAAAEEEEAAAQQQQCDCAsnT5SIMw61nXkABPF+UVajMRwU/teCR5ro8/PDD3bs+qygD8Pzzzy8wlNufr/eJEye6rFFtX3XVVXGHWutYUYsWmPBDwJW5unjx4gJNKQiozFUVzbGpoixLZVCGi7IYfWBSwUS/KJOGkSs4qFK5cmWXkRkb/NSxzp0724ABA7RpCvoquOgDyW5nzB9aAErzaoaL7uWMM84I73LzzIWDnzqoeUY1B50vixYt8pvuferUqaZ+q2gOVw1fDwc/tV/z1el5tGvXTh9dMFtzmiYq8lD9cPBTddVnBbj1rqLM0mT37SrxBwIIIIAAAggggAACCCCAAAII5L0AAdC8f8S5e4PhAKiyOxXQ0lB4BQE1TFrBLr1rFVwfDNOclFo8KLZoLtFx48a53aecckqBLMbYusX5HM5+jM321FyXynZUOeuss4LLaIh4uGjle1/CCwNpuLmGd6voHurWreurbfN+1FFHuSHgOqCMUc0NmqiceeaZQZA1XEdDyMPluOOOC38Mtps0aRJsK2s3XJSF6Us4w9TvC78rM1RF9/jyyy+HDxXY1uJMiea60/yhzZs3d/XVTni+0gKN5MgHZRTvtdde7qVFnigIIIAAAggggAACCCCAAAIIIJC+AAHQ9M04o4wEtECPLwpmKdtQq7X7YKc/1rJlSxf01CroKppL0mda6rOCjhoqrjYUHOvTp492l0pRBqjPSFXANlx8QLRRo0ZuWLqvp8zVcJk5c6b7qPvRsHpftBCQL8raTFbUts9GVb3wubHnhYfYh4/VrFkz+Ch7ZWrGK8pG9SX2mfksWN1zslXpdX7savO+zdj3whbDCvc73J/YdnLhMwHQXHhK9BEBBBBAAAEEEEAAAQQQQCDbBZgDNNufUIT7V7Vq1QJ337dvX4vd5yvUrl3bZVWOHTvW7dIK5j54eM8997gAoIKC1157bcJAnm+rOO8+aKm5PRXY1LyUfki2D4BqGLmCicpi1KI/4XlAFbBTpqeKgpz+XH3+5n9DybWtgGJhJVwnUQC0YsWKbjh9vLb80HsdSxa8jB3S7tvSfJ66fxXNEXrCCSf4Q4W++8Wv4lXU4knJiu7JF4bAewneEUAAAQQQQAABBBBAAAEEEIiuAAHQ6D77rL/z2GDn3nvvnbTPe+65Z3DcBws1nHzKlClu/2mnneYCh1ohPbaEMwU1bNrXqVKlSoEgZOx58T5rGLyCmmrns88+c8OXNc/n3LlzXXU/j6ZWU1cAVMPTly9fblrwSMPhtdK6Snj1d332w9gVyNWK7YUVtedL7NB0vz+cven3xXtPFOSMV9fvCwcx5asgaKoldi7R8HnKiqQggAACCCCAAAIIIIAAAggggAACqQoQAE1VinplLhAO4Cnop5XbkxW/sJDq+ODbf/7zn+CUf/zjH6ZXYeXCCy8Mqmg+0fbt2wefU9no1KmTjR492lVV1qfmcPRZndoZDoCOHz/e1VO2aPfu3YPV37XoU4cOHdwx/4eGdiuIqKH8CpLGBoh9Pf++du1av+lWcA8+hDaKEtgMnZ50Mxxcbdu2rbu/pCeEDoYzX0O72UQAAQQQQAABBBBAAAEEEEAAAQTSFiAAmjYZJ5SVQK1atVymo1ZFV9BPWYHhIGdsP3zWpvZrMZxMFQ091yr1WuF99uzZdsEFFwQBUO2vU6eO69oee+zhhp9v2rTJZYyGA6AKksYGOHXvX3/9tTt3yZIl7hrJ7lF1fNEUAWVdwosj6donnXRSWXeB6yGAAAIIIIAAAggggAACCCCAAAJGAJQvgqwW0MI4M2bMcH385JNPkgZAfdanKvv5L7UQkAKphZV///vfwRybWu3cn1PYfJOJ2lUWqAKgWoFdmZh+pXef/anzlOWoYf1aLElD5hcsWGA+aBk7/F31wwFFBUIVTE1WdH1fCls4yNcryXcFejUn6vr1651FeD7UeNdRIFhzt+rZNWjQoFTnao13ffYhgAACCCCAAAIIIIAAAggggEB+ChAAzc/nmjd31bVr1yAA+uqrr7ph1OHFecI3On369OCjDyBqPk69CivKLvXzhp566qnWtGnTwk5JelzXf/TRR02L8Dz33HPmV0MPB0DVgOYBVQB02bJlNmnSJNem7i9en9u1axdcc8KECXbkkUdaoiHsCn761eQ1j6muk4miPuv+tmzZYpMnTzbNw5qoTJ061e6++253+IgjjrBhw4Ylqsp+BBBAAAEEEEAAAQQQQAABBBBAIGWB7VKuSUUEMiCgTEplA6poQaP77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" width="672" /></p>
<div class="sourceCode" id="cb239"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb239-1"><a href="#cb239-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureB1_Ties.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>)</span></code></pre></div>
</div>
</div>
<div id="various-indicators-of-crisis" class="section level2">
<h2>Various Indicators of Crisis</h2>
<p>Currency Crisis</p>
<div class="sourceCode" id="cb240"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb240-1"><a href="#cb240-1" tabindex="-1"></a><span class="do">## Calendar year</span></span>
<span id="cb240-2"><a href="#cb240-2" tabindex="-1"></a>cc_tc <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m5, </span>
<span id="cb240-3"><a href="#cb240-3" tabindex="-1"></a>                <span class="fu">update</span>(base_fml, . <span class="sc">~</span> currency_mon0 <span class="sc">+</span> </span>
<span id="cb240-4"><a href="#cb240-4" tabindex="-1"></a>                          womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb240-5"><a href="#cb240-5" tabindex="-1"></a>                          presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb240-6"><a href="#cb240-6" tabindex="-1"></a>                          growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb240-7"><a href="#cb240-7" tabindex="-1"></a>                          tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag))</span></code></pre></div>
<p>Inflation crisis</p>
<div class="sourceCode" id="cb241"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb241-1"><a href="#cb241-1" tabindex="-1"></a>fml_inf <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(fwfm72_t0, fwfm72_t, fwfm72_d) <span class="sc">~</span>  womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb241-2"><a href="#cb241-2" tabindex="-1"></a>                     presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb241-3"><a href="#cb241-3" tabindex="-1"></a>                     growth_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb241-4"><a href="#cb241-4" tabindex="-1"></a>                     tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag)</span>
<span id="cb241-5"><a href="#cb241-5" tabindex="-1"></a>cox_i0 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(fml_inf, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> .), </span>
<span id="cb241-6"><a href="#cb241-6" tabindex="-1"></a>                 <span class="at">data =</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(crisis20)))</span></code></pre></div>
<pre><code>## filter: removed 4,000 rows (4%), 96,512 rows remaining
## filter: removed 4,000 rows (4%), 96,512 rows remaining</code></pre>
<div class="sourceCode" id="cb243"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb243-1"><a href="#cb243-1" tabindex="-1"></a>cox_i1 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(fml_inf, . <span class="sc">~</span> crisis20 <span class="sc">+</span> .), <span class="at">data =</span> df)</span></code></pre></div>
<p>Unemployment crisis</p>
<p>As the data are available only for 1990-2017, we will compare this
against 1990 data.</p>
<div class="sourceCode" id="cb244"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb244-1"><a href="#cb244-1" tabindex="-1"></a>cox_u0 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(fml_inf, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> .), </span>
<span id="cb244-2"><a href="#cb244-2" tabindex="-1"></a>                 <span class="at">data =</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(cri_avgsd5_roll)))</span></code></pre></div>
<pre><code>## filter: removed 55,896 rows (56%), 44,616 rows remaining
## filter: removed 55,896 rows (56%), 44,616 rows remaining</code></pre>
<div class="sourceCode" id="cb246"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb246-1"><a href="#cb246-1" tabindex="-1"></a>cox_u1 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(fml_inf, . <span class="sc">~</span> cri_avgsd5_roll <span class="sc">+</span> .), <span class="at">data =</span> df)</span></code></pre></div>
<div id="table-b4" class="section level3">
<h3>Table B4</h3>
<p>Combine baseline results and currency, inflation, and unemployment
crises models.</p>
<div class="sourceCode" id="cb247"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb247-1"><a href="#cb247-1" tabindex="-1"></a>model_list_tblB4 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, cc_tc, cox_i0, cox_i1, cox_u0, cox_u1)</span>
<span id="cb247-2"><a href="#cb247-2" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb247-3"><a href="#cb247-3" tabindex="-1"></a>model_list_tblB4 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB4, add_AIC)</span></code></pre></div>
<div class="sourceCode" id="cb248"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb248-1"><a href="#cb248-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB4,</span>
<span id="cb248-2"><a href="#cb248-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB4, get_clse_hr),</span>
<span id="cb248-3"><a href="#cb248-3" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb248-4"><a href="#cb248-4" tabindex="-1"></a>                               <span class="st">&quot;Currency Crisis (calendar year)&quot;</span>,</span>
<span id="cb248-5"><a href="#cb248-5" tabindex="-1"></a>                               <span class="st">&quot;Inflation Crisis ( &gt; 20%)&quot;</span>,</span>
<span id="cb248-6"><a href="#cb248-6" tabindex="-1"></a>                               <span class="st">&quot;Unemployment Crisis (&gt; 1 s.d.)&quot;</span>,</span>
<span id="cb248-7"><a href="#cb248-7" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb248-8"><a href="#cb248-8" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>,</span>
<span id="cb248-9"><a href="#cb248-9" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb248-10"><a href="#cb248-10" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>,</span>
<span id="cb248-11"><a href="#cb248-11" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb248-12"><a href="#cb248-12" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb248-13"><a href="#cb248-13" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb248-14"><a href="#cb248-14" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb248-15"><a href="#cb248-15" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B4: Women’s Initial Access: Various Indicators of Crisis, 1972-2017&quot;</span>,</span>
<span id="cb248-16"><a href="#cb248-16" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb248-17"><a href="#cb248-17" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB4.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B4: Women’s Initial Access: Various Indicators of Crisis,
1972-2017</strong>
</caption>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="6">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
<td>
(5)
</td>
<td>
(6)
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.130<sup>**</sup>
</td>
<td>
</td>
<td>
1.125<sup>**</sup>
</td>
<td>
</td>
<td>
1.257<sup>**</sup>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.448)
</td>
<td>
</td>
<td>
(0.450)
</td>
<td>
</td>
<td>
(0.500)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Currency Crisis (calendar year)
</td>
<td>
</td>
<td>
1.416<sup>**</sup>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.581)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation Crisis ( &gt; 20%)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.721<sup>*</sup>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.402)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unemployment Crisis (&gt; 1 s.d.)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.596
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.499)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.052<sup>***</sup>
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.052<sup>***</sup>
</td>
<td>
0.054<sup>***</sup>
</td>
<td>
0.055<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.013)
</td>
<td>
(0.012)
</td>
<td>
(0.013)
</td>
<td>
(0.012)
</td>
<td>
(0.015)
</td>
<td>
(0.015)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
0.625
</td>
<td>
0.819<sup>*</sup>
</td>
<td>
0.614
</td>
<td>
0.835<sup>*</sup>
</td>
<td>
0.858
</td>
<td>
1.014<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.554)
</td>
<td>
(0.476)
</td>
<td>
(0.543)
</td>
<td>
(0.470)
</td>
<td>
(0.653)
</td>
<td>
(0.542)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.288
</td>
<td>
0.333
</td>
<td>
0.286
</td>
<td>
0.335
</td>
<td>
0.482
</td>
<td>
0.563<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.301)
</td>
<td>
(0.291)
</td>
<td>
(0.301)
</td>
<td>
(0.292)
</td>
<td>
(0.344)
</td>
<td>
(0.330)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.910<sup>***</sup>
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.950<sup>***</sup>
</td>
<td>
1.055<sup>***</sup>
</td>
<td>
1.153<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.245)
</td>
<td>
(0.240)
</td>
<td>
(0.245)
</td>
<td>
(0.239)
</td>
<td>
(0.386)
</td>
<td>
(0.379)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.054
</td>
<td>
-0.075
</td>
<td>
-0.057
</td>
<td>
-0.065
</td>
<td>
0.311
</td>
<td>
0.274
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.294)
</td>
<td>
(0.284)
</td>
<td>
(0.293)
</td>
<td>
(0.287)
</td>
<td>
(0.330)
</td>
<td>
(0.332)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.034
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.035
</td>
<td>
0.053<sup>**</sup>
</td>
<td>
0.037
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.022)
</td>
<td>
(0.027)
</td>
<td>
(0.022)
</td>
<td>
(0.026)
</td>
<td>
(0.022)
</td>
<td>
(0.031)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
-0.020
</td>
<td>
0.015
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.104)
</td>
<td>
(0.091)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.374<sup>**</sup>
</td>
<td>
-0.331<sup>*</sup>
</td>
<td>
-0.373<sup>**</sup>
</td>
<td>
-0.332<sup>*</sup>
</td>
<td>
-0.456<sup>*</sup>
</td>
<td>
-0.404<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.183)
</td>
<td>
(0.177)
</td>
<td>
(0.183)
</td>
<td>
(0.174)
</td>
<td>
(0.247)
</td>
<td>
(0.244)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
0.001
</td>
<td>
0.001
</td>
<td>
0.001
</td>
<td>
0.001
</td>
<td>
-0.001
</td>
<td>
-0.0004
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.098
</td>
<td>
-0.109
</td>
<td>
-0.098
</td>
<td>
-0.092
</td>
<td>
-0.077
</td>
<td>
-0.135
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.147)
</td>
<td>
(0.143)
</td>
<td>
(0.147)
</td>
<td>
(0.149)
</td>
<td>
(0.164)
</td>
<td>
(0.154)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
53,805
</td>
<td>
53,805
</td>
<td>
53,817
</td>
<td>
53,817
</td>
<td>
27,857
</td>
<td>
27,857
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-222.850
</td>
<td>
-223.730
</td>
<td>
-222.859
</td>
<td>
-224.560
</td>
<td>
-141.781
</td>
<td>
-143.866
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
467.699
</td>
<td>
469.460
</td>
<td>
465.717
</td>
<td>
469.119
</td>
<td>
303.561
</td>
<td>
307.733
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="6" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>We calculate N for each model. We first prepare smaller data sets for
different models.</p>
<div class="sourceCode" id="cb249"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb249-1"><a href="#cb249-1" tabindex="-1"></a>df_i0 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, crisis20, iso3c, </span>
<span id="cb249-2"><a href="#cb249-2" tabindex="-1"></a>                      womenrep_lag, leadgender,</span>
<span id="cb249-3"><a href="#cb249-3" tabindex="-1"></a>                      presidential, xm_qudsest, unified_aut,</span>
<span id="cb249-4"><a href="#cb249-4" tabindex="-1"></a>                      growth_lag, loggdppc_lag, </span>
<span id="cb249-5"><a href="#cb249-5" tabindex="-1"></a>                      tradepergdp_i_lag, kaopen_i_lag) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span>
<span id="cb249-6"><a href="#cb249-6" tabindex="-1"></a>df_u0 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">select</span>(<span class="at">fail =</span> fwfm72_d, crisis_mon, cri_avgsd5_roll, iso3c, </span>
<span id="cb249-7"><a href="#cb249-7" tabindex="-1"></a>                      womenrep_lag, leadgender,</span>
<span id="cb249-8"><a href="#cb249-8" tabindex="-1"></a>                      presidential, xm_qudsest, unified_aut,</span>
<span id="cb249-9"><a href="#cb249-9" tabindex="-1"></a>                      growth_lag, loggdppc_lag, </span>
<span id="cb249-10"><a href="#cb249-10" tabindex="-1"></a>                      tradepergdp_i_lag, kaopen_i_lag) <span class="sc">|&gt;</span> <span class="fu">na.omit</span>()</span></code></pre></div>
<div class="sourceCode" id="cb250"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb250-1"><a href="#cb250-1" tabindex="-1"></a>res_n <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">list</span>(df_m5, df_i0, df_u0), report_n)</span>
<span id="cb250-2"><a href="#cb250-2" tabindex="-1"></a>res_n <span class="ot">&lt;-</span> <span class="fu">unlist</span>(res_n) <span class="sc">|&gt;</span> <span class="fu">matrix</span>(<span class="at">byrow =</span> F, <span class="at">ncol =</span> <span class="dv">3</span>) <span class="sc">|&gt;</span> <span class="fu">data.frame</span>()</span>
<span id="cb250-3"><a href="#cb250-3" tabindex="-1"></a><span class="fu">rownames</span>(res_n) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;N. Obs&quot;</span>, <span class="st">&quot;N. Country&quot;</span>, <span class="st">&quot;N. Failures&quot;</span>)</span>
<span id="cb250-4"><a href="#cb250-4" tabindex="-1"></a><span class="fu">colnames</span>(res_n) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Models 1-2&quot;</span>, <span class="st">&quot;Model3 3-4&quot;</span>, <span class="st">&quot;Models 5-6&quot;</span>)</span>
<span id="cb250-5"><a href="#cb250-5" tabindex="-1"></a><span class="fu">kable</span>(res_n)</span></code></pre></div>
<table>
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Models 1-2
</th>
<th style="text-align:right;">
Model3 3-4
</th>
<th style="text-align:right;">
Models 5-6
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
N. Obs
</td>
<td style="text-align:right;">
53805
</td>
<td style="text-align:right;">
53817
</td>
<td style="text-align:right;">
27857
</td>
</tr>
<tr>
<td style="text-align:left;">
N. Country
</td>
<td style="text-align:right;">
150
</td>
<td style="text-align:right;">
150
</td>
<td style="text-align:right;">
138
</td>
</tr>
<tr>
<td style="text-align:left;">
N. Failures
</td>
<td style="text-align:right;">
55
</td>
<td style="text-align:right;">
55
</td>
<td style="text-align:right;">
39
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="woman-hog-historical" class="section level2">
<h2>Woman HoG (Historical)</h2>
<p>We control for whether a country has ever had a woman head of
government.</p>
<p>Identify the year in which a country had a woman head of government
for the first time.</p>
<div class="sourceCode" id="cb251"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb251-1"><a href="#cb251-1" tabindex="-1"></a><span class="do">## Find first year with woman HoG</span></span>
<span id="cb251-2"><a href="#cb251-2" tabindex="-1"></a>first_whog_year <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb251-3"><a href="#cb251-3" tabindex="-1"></a>  <span class="fu">filter</span>(leadgender <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb251-4"><a href="#cb251-4" tabindex="-1"></a>  <span class="fu">group_by</span>(iso3c) <span class="sc">|&gt;</span> </span>
<span id="cb251-5"><a href="#cb251-5" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">first_whog_year =</span> <span class="fu">min</span>(year))</span></code></pre></div>
<pre><code>## filter: removed 96,938 rows (96%), 3,574 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (iso3c)</code></pre>
<pre><code>## summarise: now 48 rows and 2 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb255"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb255-1"><a href="#cb255-1" tabindex="-1"></a><span class="do">## Merge this and define &quot;woman HoG ever&quot;</span></span>
<span id="cb255-2"><a href="#cb255-2" tabindex="-1"></a>yd_whog <span class="ot">&lt;-</span> <span class="fu">left_join</span>(df, first_whog_year)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(iso3c)`</code></pre>
<pre><code>## left_join: added one column (first_whog_year)</code></pre>
<pre><code>##            &gt; rows only in x    76,395</code></pre>
<pre><code>##            &gt; rows only in y  (      0)</code></pre>
<pre><code>##            &gt; matched rows      24,117</code></pre>
<pre><code>##            &gt;                 =========</code></pre>
<pre><code>##            &gt; rows total       100,512</code></pre>
<div class="sourceCode" id="cb263"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb263-1"><a href="#cb263-1" tabindex="-1"></a>yd_whog <span class="sc">$</span> first_whog_year[<span class="fu">is.na</span>(yd_whog <span class="sc">$</span> first_whog_year)] <span class="ot">&lt;-</span> <span class="dv">9999</span></span>
<span id="cb263-2"><a href="#cb263-2" tabindex="-1"></a><span class="do">## Those that had woman HoG in 1966-1971. </span></span>
<span id="cb263-3"><a href="#cb263-3" tabindex="-1"></a>yd_whog <span class="sc">$</span> first_whog_year[yd_whog <span class="sc">$</span> iso3c <span class="sc">==</span> <span class="st">&quot;IND&quot;</span>] <span class="ot">&lt;-</span> <span class="dv">1966</span></span>
<span id="cb263-4"><a href="#cb263-4" tabindex="-1"></a>yd_whog <span class="sc">$</span> first_whog_year[yd_whog <span class="sc">$</span> iso3c <span class="sc">==</span> <span class="st">&quot;ISR&quot;</span>] <span class="ot">&lt;-</span> <span class="dv">1969</span></span>
<span id="cb263-5"><a href="#cb263-5" tabindex="-1"></a>yd_whog <span class="sc">$</span> first_whog_year[yd_whog <span class="sc">$</span> iso3c <span class="sc">==</span> <span class="st">&quot;LKA&quot;</span>] <span class="ot">&lt;-</span> <span class="dv">1971</span></span>
<span id="cb263-6"><a href="#cb263-6" tabindex="-1"></a>yd_whog <span class="ot">&lt;-</span> yd_whog <span class="sc">|&gt;</span> </span>
<span id="cb263-7"><a href="#cb263-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">whog_ever =</span> <span class="fu">if_else</span>(year <span class="sc">&gt;=</span> first_whog_year, <span class="dv">1</span>, <span class="dv">0</span>),</span>
<span id="cb263-8"><a href="#cb263-8" tabindex="-1"></a>         <span class="at">crisis_whoge =</span> <span class="fu">if_else</span>(whog_ever <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;whog=0, cr=0&quot;</span>, <span class="st">&quot;NA&quot;</span>),</span>
<span id="cb263-9"><a href="#cb263-9" tabindex="-1"></a>         <span class="at">crisis_whoge =</span> <span class="fu">if_else</span>(whog_ever <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;whog=0, cr=1&quot;</span>, crisis_whoge),</span>
<span id="cb263-10"><a href="#cb263-10" tabindex="-1"></a>         <span class="at">crisis_whoge =</span> <span class="fu">if_else</span>(whog_ever <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;whog=1, cr=0&quot;</span>, crisis_whoge),</span>
<span id="cb263-11"><a href="#cb263-11" tabindex="-1"></a>         <span class="at">crisis_whoge =</span> <span class="fu">if_else</span>(whog_ever <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;whog=1, cr=1&quot;</span>, crisis_whoge),</span>
<span id="cb263-12"><a href="#cb263-12" tabindex="-1"></a>         <span class="at">crisis_whogeAlt =</span> <span class="fu">if_else</span>(crisis_whoge <span class="sc">==</span> <span class="st">&quot;whog=1, cr=1&quot;</span>, <span class="st">&quot;0whog=1, cr=1&quot;</span>, crisis_whoge))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;whog_ever&#39; (double) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis_whoge&#39; (character) with 4 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis_whogeAlt&#39; (character) with 4 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb267"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb267-1"><a href="#cb267-1" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_mon, whog_ever, useNA = &quot;ifany&quot;))</span></span>
<span id="cb267-2"><a href="#cb267-2" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_whoge, useNA = &quot;ifany&quot;))</span></span>
<span id="cb267-3"><a href="#cb267-3" tabindex="-1"></a><span class="co"># with(yd_whog, table(leadgender, whog_ever, useNA = &quot;ifany&quot;))</span></span>
<span id="cb267-4"><a href="#cb267-4" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_whoge, crisis_whogeAlt, useNA = &quot;ifany&quot;))</span></span></code></pre></div>
<p>Estimate models that control for woman head of government
variables.</p>
<div class="sourceCode" id="cb268"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb268-1"><a href="#cb268-1" tabindex="-1"></a>rhs_cov <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(fwfm72_t0, fwfm72_t, fwfm72_d) <span class="sc">~</span>  womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb268-2"><a href="#cb268-2" tabindex="-1"></a>                     presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb268-3"><a href="#cb268-3" tabindex="-1"></a>                     growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb268-4"><a href="#cb268-4" tabindex="-1"></a>                     tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag)</span>
<span id="cb268-5"><a href="#cb268-5" tabindex="-1"></a></span>
<span id="cb268-6"><a href="#cb268-6" tabindex="-1"></a><span class="do">## Include binary woman HoG ever</span></span>
<span id="cb268-7"><a href="#cb268-7" tabindex="-1"></a>wia_m15 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> whog_ever <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb268-8"><a href="#cb268-8" tabindex="-1"></a><span class="do">## Interact with binary woman HoG ever</span></span>
<span id="cb268-9"><a href="#cb268-9" tabindex="-1"></a>wia_m16 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_mon <span class="sc">*</span> whog_ever <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb268-10"><a href="#cb268-10" tabindex="-1"></a><span class="do">## Alternative specifications (same models, different representation)</span></span>
<span id="cb268-11"><a href="#cb268-11" tabindex="-1"></a><span class="do">## We need these to obtain p-values for interaction terms.</span></span>
<span id="cb268-12"><a href="#cb268-12" tabindex="-1"></a>wia_m16a <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_whoge <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb268-13"><a href="#cb268-13" tabindex="-1"></a>wia_m16b <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_whogeAlt <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb268-14"><a href="#cb268-14" tabindex="-1"></a></span>
<span id="cb268-15"><a href="#cb268-15" tabindex="-1"></a><span class="do">## Put together results</span></span>
<span id="cb268-16"><a href="#cb268-16" tabindex="-1"></a>model_list_tblB5c <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m15, wia_m16, wia_m16a, wia_m16b)</span>
<span id="cb268-17"><a href="#cb268-17" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb268-18"><a href="#cb268-18" tabindex="-1"></a>model_list_tblB5c <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB5c, add_AIC)</span></code></pre></div>
<p>Check results</p>
<div id="table-b5" class="section level3">
<h3>Table B5</h3>
<div class="sourceCode" id="cb269"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb269-1"><a href="#cb269-1" tabindex="-1"></a>model_list_tblB5 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m15, wia_m16a)</span>
<span id="cb269-2"><a href="#cb269-2" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb269-3"><a href="#cb269-3" tabindex="-1"></a>model_list_tblB5 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB5, add_AIC)</span>
<span id="cb269-4"><a href="#cb269-4" tabindex="-1"></a></span>
<span id="cb269-5"><a href="#cb269-5" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB5,</span>
<span id="cb269-6"><a href="#cb269-6" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB5, get_clse_hr),</span>
<span id="cb269-7"><a href="#cb269-7" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb269-8"><a href="#cb269-8" tabindex="-1"></a>                               <span class="st">&quot;Had Woman HoG Ever&quot;</span>, </span>
<span id="cb269-9"><a href="#cb269-9" tabindex="-1"></a>                               <span class="st">&quot;WHoGE = 0, Crisis = 1&quot;</span>, </span>
<span id="cb269-10"><a href="#cb269-10" tabindex="-1"></a>                               <span class="st">&quot;WHoGE = 1, Crisis = 0&quot;</span>,</span>
<span id="cb269-11"><a href="#cb269-11" tabindex="-1"></a>                               <span class="st">&quot;WHoGE = 1, Crisis = 1&quot;</span>,</span>
<span id="cb269-12"><a href="#cb269-12" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb269-13"><a href="#cb269-13" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, </span>
<span id="cb269-14"><a href="#cb269-14" tabindex="-1"></a>                               <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb269-15"><a href="#cb269-15" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb269-16"><a href="#cb269-16" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb269-17"><a href="#cb269-17" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb269-18"><a href="#cb269-18" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb269-19"><a href="#cb269-19" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb269-20"><a href="#cb269-20" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb269-21"><a href="#cb269-21" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B5: Conditional Effect of Banking Crisis: Woman HoG (Historical), 1972-2017&quot;</span>,</span>
<span id="cb269-22"><a href="#cb269-22" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>, </span>
<span id="cb269-23"><a href="#cb269-23" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB5.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B5: Conditional Effect of Banking Crisis: Woman HoG
(Historical), 1972-2017</strong>
</caption>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="3">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.130<sup>**</sup>
</td>
<td>
1.120<sup>**</sup>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.448)
</td>
<td>
(0.456)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Had Woman HoG Ever
</td>
<td>
</td>
<td>
0.167
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.478)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
WHoGE = 0, Crisis = 1
</td>
<td>
</td>
<td>
</td>
<td>
1.670<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.444)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
WHoGE = 1, Crisis = 0
</td>
<td>
</td>
<td>
</td>
<td>
0.588
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.510)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
WHoGE = 1, Crisis = 1
</td>
<td>
</td>
<td>
</td>
<td>
-0.686
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.788)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.055<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
0.625
</td>
<td>
0.476
</td>
<td>
0.688
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.554)
</td>
<td>
(0.693)
</td>
<td>
(0.648)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.288
</td>
<td>
0.290
</td>
<td>
0.457
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.301)
</td>
<td>
(0.301)
</td>
<td>
(0.297)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.890<sup>***</sup>
</td>
<td>
0.981<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.245)
</td>
<td>
(0.246)
</td>
<td>
(0.265)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.054
</td>
<td>
-0.063
</td>
<td>
-0.103
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.294)
</td>
<td>
(0.300)
</td>
<td>
(0.309)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.042<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.022)
</td>
<td>
(0.022)
</td>
<td>
(0.025)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
-0.020
</td>
<td>
-0.022
</td>
<td>
0.059
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.104)
</td>
<td>
(0.103)
</td>
<td>
(0.060)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.374<sup>**</sup>
</td>
<td>
-0.379<sup>**</sup>
</td>
<td>
-0.353<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.183)
</td>
<td>
(0.184)
</td>
<td>
(0.191)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
0.001
</td>
<td>
0.001
</td>
<td>
0.001
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.098
</td>
<td>
-0.104
</td>
<td>
-0.214
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.147)
</td>
<td>
(0.147)
</td>
<td>
(0.152)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
53,805
</td>
<td>
53,805
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-222.850
</td>
<td>
-222.796
</td>
<td>
-218.767
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
467.699
</td>
<td>
469.591
</td>
<td>
463.533
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="3" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Obtain p-values using the models with alternative specifications.</p>
</div>
<div id="figure-b2" class="section level3">
<h3>Figure B2</h3>
<div class="sourceCode" id="cb270"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb270-1"><a href="#cb270-1" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> <span class="fu">summary</span>(wia_m16a) <span class="sc">$</span> conf.int[<span class="fu">c</span>(<span class="st">&quot;crisis_whogewhog=0, cr=1&quot;</span>, </span>
<span id="cb270-2"><a href="#cb270-2" tabindex="-1"></a>                                       <span class="st">&quot;crisis_whogewhog=1, cr=0&quot;</span>, </span>
<span id="cb270-3"><a href="#cb270-3" tabindex="-1"></a>                                       <span class="st">&quot;crisis_whogewhog=1, cr=1&quot;</span>),] <span class="sc">|&gt;</span> </span>
<span id="cb270-4"><a href="#cb270-4" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">|&gt;</span> </span>
<span id="cb270-5"><a href="#cb270-5" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">hr =</span> <span class="st">&quot;exp(coef)&quot;</span>, <span class="at">low =</span> <span class="st">&quot;lower .95&quot;</span>, <span class="at">up =</span> <span class="st">&quot;upper .95&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb270-6"><a href="#cb270-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">womanHoG =</span> <span class="fu">c</span>(<span class="st">&quot;No&quot;</span>, <span class="st">&quot;Yes&quot;</span>, <span class="st">&quot;Yes&quot;</span>),</span>
<span id="cb270-7"><a href="#cb270-7" tabindex="-1"></a>         <span class="at">crisis =</span> <span class="fu">c</span>(<span class="st">&quot;Crisis = 1&quot;</span>, <span class="st">&quot;Crisis = 0&quot;</span>, <span class="st">&quot;Crisis = 1&quot;</span>), </span>
<span id="cb270-8"><a href="#cb270-8" tabindex="-1"></a>         <span class="at">gc =</span> <span class="fu">c</span>(<span class="st">&quot;No woman HoG ever</span><span class="sc">\n</span><span class="st">Crisis&quot;</span>, </span>
<span id="cb270-9"><a href="#cb270-9" tabindex="-1"></a>                <span class="st">&quot;Woman HoG before</span><span class="sc">\n</span><span class="st">No Crisis&quot;</span>, <span class="st">&quot;Woman HoG before</span><span class="sc">\n</span><span class="st">Crisis&quot;</span>),</span>
<span id="cb270-10"><a href="#cb270-10" tabindex="-1"></a>         <span class="at">signif =</span> <span class="fu">if_else</span>((low <span class="sc">&gt;</span> <span class="dv">1</span> <span class="sc">&amp;</span> up <span class="sc">&gt;</span> <span class="dv">1</span>) <span class="sc">|</span> (low <span class="sc">&lt;</span> <span class="dv">1</span> <span class="sc">&amp;</span> up <span class="sc">&lt;</span> <span class="dv">1</span>), <span class="st">&quot;Yes&quot;</span>, <span class="st">&quot;No&quot;</span>),</span>
<span id="cb270-11"><a href="#cb270-11" tabindex="-1"></a>         <span class="at">mnum =</span> <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">2</span>,<span class="dv">1</span>))</span></code></pre></div>
<pre><code>## select: renamed 3 variables (hr, low, up) and dropped one variable</code></pre>
<pre><code>## mutate: new variable &#39;womanHoG&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;gc&#39; (character) with 3 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;signif&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;mnum&#39; (double) with 3 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb277"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb277-1"><a href="#cb277-1" tabindex="-1"></a>df_hr</span></code></pre></div>
<pre><code>##                                 hr       low        up womanHoG     crisis
## crisis_whogewhog=0, cr=1 5.3142082 2.2240063 12.698169       No Crisis = 1
## crisis_whogewhog=1, cr=0 1.7998380 0.6626772  4.888378      Yes Crisis = 0
## crisis_whogewhog=1, cr=1 0.5036934 0.1074728  2.360663      Yes Crisis = 1
##                                                   gc signif mnum
## crisis_whogewhog=0, cr=1   No woman HoG ever\nCrisis    Yes    3
## crisis_whogewhog=1, cr=0 Woman HoG before\nNo Crisis     No    2
## crisis_whogewhog=1, cr=1    Woman HoG before\nCrisis     No    1</code></pre>
<div class="sourceCode" id="cb279"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb279-1"><a href="#cb279-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> df_hr, </span>
<span id="cb279-2"><a href="#cb279-2" tabindex="-1"></a>       <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> hr, <span class="at">y =</span> <span class="fu">reorder</span>(gc, mnum))) <span class="sc">+</span> </span>
<span id="cb279-3"><a href="#cb279-3" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="dv">2</span>, <span class="at">alpha =</span> <span class="fl">0.4</span>) <span class="sc">+</span> </span>
<span id="cb279-4"><a href="#cb279-4" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> low, <span class="at">xend =</span> up, <span class="at">yend =</span> <span class="fu">reorder</span>(gc, mnum),</span>
<span id="cb279-5"><a href="#cb279-5" tabindex="-1"></a>                   <span class="at">color =</span> signif), <span class="at">size =</span> <span class="fl">1.5</span>) <span class="sc">+</span> </span>
<span id="cb279-6"><a href="#cb279-6" tabindex="-1"></a>  <span class="fu">geom_label</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">round</span>(hr, <span class="dv">2</span>), <span class="at">color =</span> signif)) <span class="sc">+</span> </span>
<span id="cb279-7"><a href="#cb279-7" tabindex="-1"></a>  <span class="fu">xlim</span>(<span class="dv">0</span>, <span class="dv">15</span>) <span class="sc">+</span></span>
<span id="cb279-8"><a href="#cb279-8" tabindex="-1"></a>  <span class="fu">scale_color_grey</span>(<span class="at">start =</span> <span class="fl">0.6</span>, <span class="at">end =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb279-9"><a href="#cb279-9" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span> </span>
<span id="cb279-10"><a href="#cb279-10" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;&quot;</span>, <span class="at">x =</span> <span class="st">&quot;Hazard ratio (no woman HoG &amp; no crisis as baseline)&quot;</span>) <span class="sc">+</span> </span>
<span id="cb279-11"><a href="#cb279-11" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb279-12"><a href="#cb279-12" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="dv">6</span>, <span class="at">xend =</span> <span class="dv">6</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb279-13"><a href="#cb279-13" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">5.5</span>, <span class="at">xend =</span> <span class="dv">6</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb279-14"><a href="#cb279-14" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">5.5</span>, <span class="at">xend =</span> <span class="dv">6</span>, <span class="at">y =</span> <span class="dv">2</span>, <span class="at">yend =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb279-15"><a href="#cb279-15" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="dv">5</span>,  <span class="at">y =</span> <span class="fl">1.5</span>, <span class="at">label =</span> <span class="st">&quot;p = 0.06&quot;</span>, <span class="at">angle =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb279-16"><a href="#cb279-16" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="dv">14</span>, <span class="at">xend =</span> <span class="dv">14</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb279-17"><a href="#cb279-17" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">13.5</span>, <span class="at">xend =</span> <span class="dv">14</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb279-18"><a href="#cb279-18" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">13.5</span>, <span class="at">xend =</span> <span class="dv">14</span>, <span class="at">y =</span> <span class="dv">3</span>, <span class="at">yend =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb279-19"><a href="#cb279-19" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="fl">12.8</span>,  <span class="at">y =</span> <span class="dv">2</span>, <span class="at">label =</span> <span class="st">&quot;p = 0.006&quot;</span>, <span class="at">angle =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb279-20"><a href="#cb279-20" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">10</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb280"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb280-1"><a href="#cb280-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureB2_WHoGhistorical.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>)</span></code></pre></div>
</div>
</div>
<div id="current-woman-hog" class="section level2">
<h2>Current Woman HoG</h2>
<p>We control for whether a country currently has a woman head of
government.</p>
<div class="sourceCode" id="cb281"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb281-1"><a href="#cb281-1" tabindex="-1"></a>yd_whog <span class="ot">&lt;-</span> yd_whog <span class="sc">|&gt;</span> </span>
<span id="cb281-2"><a href="#cb281-2" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">crisis_whogc =</span> <span class="fu">if_else</span>(leadgender <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;whog=0, cr=0&quot;</span>, <span class="st">&quot;NA&quot;</span>),</span>
<span id="cb281-3"><a href="#cb281-3" tabindex="-1"></a>         <span class="at">crisis_whogc =</span> <span class="fu">if_else</span>(leadgender <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;whog=0, cr=1&quot;</span>, crisis_whogc),</span>
<span id="cb281-4"><a href="#cb281-4" tabindex="-1"></a>         <span class="at">crisis_whogc =</span> <span class="fu">if_else</span>(leadgender <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;whog=1, cr=0&quot;</span>, crisis_whogc),</span>
<span id="cb281-5"><a href="#cb281-5" tabindex="-1"></a>         <span class="at">crisis_whogc =</span> <span class="fu">if_else</span>(leadgender <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;whog=1, cr=1&quot;</span>, crisis_whogc),</span>
<span id="cb281-6"><a href="#cb281-6" tabindex="-1"></a>         <span class="at">crisis_whogcAlt =</span> <span class="fu">if_else</span>(crisis_whogc <span class="sc">==</span> <span class="st">&quot;whog=1, cr=1&quot;</span>, <span class="st">&quot;0whog=1, cr=1&quot;</span>, crisis_whogc))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;crisis_whogc&#39; (character) with 4 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis_whogcAlt&#39; (character) with 4 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb284"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb284-1"><a href="#cb284-1" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_whogc, useNA = &quot;ifany&quot;))</span></span>
<span id="cb284-2"><a href="#cb284-2" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_whogc, crisis_mon, useNA = &quot;ifany&quot;))</span></span>
<span id="cb284-3"><a href="#cb284-3" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_whogc, leadgender, useNA = &quot;ifany&quot;))</span></span>
<span id="cb284-4"><a href="#cb284-4" tabindex="-1"></a><span class="co"># with(yd_whog, table(crisis_mon, leadgender, useNA = &quot;ifany&quot;))</span></span>
<span id="cb284-5"><a href="#cb284-5" tabindex="-1"></a></span>
<span id="cb284-6"><a href="#cb284-6" tabindex="-1"></a>rhs_cov <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(fwfm72_t0, fwfm72_t, fwfm72_d) <span class="sc">~</span>  womenrep_lag <span class="sc">+</span> </span>
<span id="cb284-7"><a href="#cb284-7" tabindex="-1"></a>                     presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb284-8"><a href="#cb284-8" tabindex="-1"></a>                     growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb284-9"><a href="#cb284-9" tabindex="-1"></a>                     tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag)</span>
<span id="cb284-10"><a href="#cb284-10" tabindex="-1"></a></span>
<span id="cb284-11"><a href="#cb284-11" tabindex="-1"></a><span class="do">## Interact with binary woman HoG</span></span>
<span id="cb284-12"><a href="#cb284-12" tabindex="-1"></a>wia_m17 <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_mon <span class="sc">*</span> leadgender <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb284-13"><a href="#cb284-13" tabindex="-1"></a>wia_m17a <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_whogc <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb284-14"><a href="#cb284-14" tabindex="-1"></a>wia_m17b <span class="ot">&lt;-</span> <span class="fu">update</span>(wia_m1, <span class="fu">update</span>(rhs_cov, . <span class="sc">~</span> crisis_whogcAlt <span class="sc">+</span> .), <span class="at">data =</span> yd_whog)</span>
<span id="cb284-15"><a href="#cb284-15" tabindex="-1"></a></span>
<span id="cb284-16"><a href="#cb284-16" tabindex="-1"></a>model_list_tblB6c <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m17, wia_m17a, wia_m17b)</span>
<span id="cb284-17"><a href="#cb284-17" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb284-18"><a href="#cb284-18" tabindex="-1"></a>model_list_tblB6c <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB6c, add_AIC)</span></code></pre></div>
<p>Check results</p>
<div id="table-b6" class="section level3">
<h3>Table B6</h3>
<div class="sourceCode" id="cb285"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb285-1"><a href="#cb285-1" tabindex="-1"></a>model_list_tblB6 <span class="ot">&lt;-</span> <span class="fu">list</span>(wia_m5, wia_m17a)</span>
<span id="cb285-2"><a href="#cb285-2" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb285-3"><a href="#cb285-3" tabindex="-1"></a>model_list_tblB6 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB6, add_AIC)</span>
<span id="cb285-4"><a href="#cb285-4" tabindex="-1"></a></span>
<span id="cb285-5"><a href="#cb285-5" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB6,</span>
<span id="cb285-6"><a href="#cb285-6" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB6, get_clse_hr),</span>
<span id="cb285-7"><a href="#cb285-7" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb285-8"><a href="#cb285-8" tabindex="-1"></a>                               <span class="st">&quot;Man HoG, Crisis = 1&quot;</span>, </span>
<span id="cb285-9"><a href="#cb285-9" tabindex="-1"></a>                               <span class="st">&quot;Woman HoG, Crisis = 0&quot;</span>,</span>
<span id="cb285-10"><a href="#cb285-10" tabindex="-1"></a>                               <span class="st">&quot;Woman HoG, Crisis = 1&quot;</span>,</span>
<span id="cb285-11"><a href="#cb285-11" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb285-12"><a href="#cb285-12" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, </span>
<span id="cb285-13"><a href="#cb285-13" tabindex="-1"></a>                               <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb285-14"><a href="#cb285-14" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb285-15"><a href="#cb285-15" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb285-16"><a href="#cb285-16" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>),</span>
<span id="cb285-17"><a href="#cb285-17" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb285-18"><a href="#cb285-18" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb285-19"><a href="#cb285-19" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb285-20"><a href="#cb285-20" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B6: Conditional Effect of Banking Crisis: Woman HoG (Current), 1972-2017&quot;</span>,</span>
<span id="cb285-21"><a href="#cb285-21" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>, </span>
<span id="cb285-22"><a href="#cb285-22" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB6.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B6: Conditional Effect of Banking Crisis: Woman HoG
(Current), 1972-2017</strong>
</caption>
<tr>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="2">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="2" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
</tr>
<tr>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
1.130<sup>**</sup>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.448)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Man HoG, Crisis = 1
</td>
<td>
</td>
<td>
1.409<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.433)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman HoG, Crisis = 0
</td>
<td>
</td>
<td>
1.164<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.510)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman HoG, Crisis = 1
</td>
<td>
</td>
<td>
0.268
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.590)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
0.051<sup>***</sup>
</td>
<td>
0.055<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.013)
</td>
<td>
(0.013)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
0.625
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.554)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.288
</td>
<td>
0.410
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.301)
</td>
<td>
(0.292)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.888<sup>***</sup>
</td>
<td>
0.959<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.245)
</td>
<td>
(0.256)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.054
</td>
<td>
-0.059
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.294)
</td>
<td>
(0.289)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
0.044<sup>**</sup>
</td>
<td>
0.041<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.022)
</td>
<td>
(0.024)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
-0.020
</td>
<td>
0.039
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.104)
</td>
<td>
(0.068)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.374<sup>**</sup>
</td>
<td>
-0.349<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.183)
</td>
<td>
(0.186)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
0.001
</td>
<td>
0.002
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.098
</td>
<td>
-0.177
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.147)
</td>
<td>
(0.150)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
53,805
</td>
<td>
53,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-222.850
</td>
<td>
-220.863
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
467.699
</td>
<td>
465.725
</td>
</tr>
<tr>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="2" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Obtain p-values</p>
</div>
<div id="figure-b3" class="section level3">
<h3>Figure B3</h3>
<div class="sourceCode" id="cb286"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb286-1"><a href="#cb286-1" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> <span class="fu">summary</span>(wia_m17a) <span class="sc">$</span> conf.int[<span class="fu">c</span>(<span class="st">&quot;crisis_whogcwhog=0, cr=1&quot;</span>, </span>
<span id="cb286-2"><a href="#cb286-2" tabindex="-1"></a>                                       <span class="st">&quot;crisis_whogcwhog=1, cr=0&quot;</span>, </span>
<span id="cb286-3"><a href="#cb286-3" tabindex="-1"></a>                                       <span class="st">&quot;crisis_whogcwhog=1, cr=1&quot;</span>),] <span class="sc">|&gt;</span> </span>
<span id="cb286-4"><a href="#cb286-4" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">|&gt;</span> </span>
<span id="cb286-5"><a href="#cb286-5" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">hr =</span> <span class="st">&quot;exp(coef)&quot;</span>, <span class="at">low =</span> <span class="st">&quot;lower .95&quot;</span>, <span class="at">up =</span> <span class="st">&quot;upper .95&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb286-6"><a href="#cb286-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">womanHoG =</span> <span class="fu">c</span>(<span class="st">&quot;No&quot;</span>, <span class="st">&quot;Yes&quot;</span>, <span class="st">&quot;Yes&quot;</span>),</span>
<span id="cb286-7"><a href="#cb286-7" tabindex="-1"></a>         <span class="at">crisis =</span> <span class="fu">c</span>(<span class="st">&quot;Crisis = 1&quot;</span>, <span class="st">&quot;Crisis = 0&quot;</span>, <span class="st">&quot;Crisis = 1&quot;</span>), </span>
<span id="cb286-8"><a href="#cb286-8" tabindex="-1"></a>         <span class="at">gc =</span> <span class="fu">c</span>(<span class="st">&quot;Man HoG</span><span class="sc">\n</span><span class="st">Crisis&quot;</span>, </span>
<span id="cb286-9"><a href="#cb286-9" tabindex="-1"></a>                <span class="st">&quot;Woman HoG</span><span class="sc">\n</span><span class="st">No Crisis&quot;</span>, <span class="st">&quot;Woman HoG</span><span class="sc">\n</span><span class="st">Crisis&quot;</span>),</span>
<span id="cb286-10"><a href="#cb286-10" tabindex="-1"></a>         <span class="at">signif =</span> <span class="fu">if_else</span>((low <span class="sc">&gt;</span> <span class="dv">1</span> <span class="sc">&amp;</span> up <span class="sc">&gt;</span> <span class="dv">1</span>) <span class="sc">|</span> (low <span class="sc">&lt;</span> <span class="dv">1</span> <span class="sc">&amp;</span> up <span class="sc">&lt;</span> <span class="dv">1</span>), <span class="st">&quot;Yes&quot;</span>, <span class="st">&quot;No&quot;</span>),</span>
<span id="cb286-11"><a href="#cb286-11" tabindex="-1"></a>         <span class="at">mnum =</span> <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">2</span>,<span class="dv">1</span>))</span></code></pre></div>
<pre><code>## select: renamed 3 variables (hr, low, up) and dropped one variable</code></pre>
<pre><code>## mutate: new variable &#39;womanHoG&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;gc&#39; (character) with 3 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;signif&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;mnum&#39; (double) with 3 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb293"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb293-1"><a href="#cb293-1" tabindex="-1"></a>df_hr</span></code></pre></div>
<pre><code>##                                hr       low       up womanHoG     crisis
## crisis_whogcwhog=0, cr=1 4.092761 1.7513840 9.564262       No Crisis = 1
## crisis_whogcwhog=1, cr=0 3.202865 1.1793498 8.698303      Yes Crisis = 0
## crisis_whogcwhog=1, cr=1 1.307213 0.4113968 4.153670      Yes Crisis = 1
##                                            gc signif mnum
## crisis_whogcwhog=0, cr=1      Man HoG\nCrisis    Yes    3
## crisis_whogcwhog=1, cr=0 Woman HoG\nNo Crisis    Yes    2
## crisis_whogcwhog=1, cr=1    Woman HoG\nCrisis     No    1</code></pre>
<div class="sourceCode" id="cb295"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb295-1"><a href="#cb295-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> df_hr, </span>
<span id="cb295-2"><a href="#cb295-2" tabindex="-1"></a>       <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> hr, <span class="at">y =</span> <span class="fu">reorder</span>(gc, mnum))) <span class="sc">+</span> </span>
<span id="cb295-3"><a href="#cb295-3" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="dv">2</span>, <span class="at">alpha =</span> <span class="fl">0.4</span>) <span class="sc">+</span> </span>
<span id="cb295-4"><a href="#cb295-4" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> low, <span class="at">xend =</span> up, <span class="at">yend =</span> <span class="fu">reorder</span>(gc, mnum),</span>
<span id="cb295-5"><a href="#cb295-5" tabindex="-1"></a>                   <span class="at">color =</span> signif), <span class="at">size =</span> <span class="fl">1.5</span>) <span class="sc">+</span> </span>
<span id="cb295-6"><a href="#cb295-6" tabindex="-1"></a>  <span class="fu">geom_label</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">round</span>(hr, <span class="dv">2</span>), <span class="at">color =</span> signif)) <span class="sc">+</span> </span>
<span id="cb295-7"><a href="#cb295-7" tabindex="-1"></a>  <span class="fu">xlim</span>(<span class="dv">0</span>, <span class="fl">12.5</span>) <span class="sc">+</span></span>
<span id="cb295-8"><a href="#cb295-8" tabindex="-1"></a>  <span class="fu">scale_color_grey</span>(<span class="at">start =</span> <span class="fl">0.6</span>, <span class="at">end =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb295-9"><a href="#cb295-9" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span> </span>
<span id="cb295-10"><a href="#cb295-10" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;&quot;</span>, <span class="at">x =</span> <span class="st">&quot;Hazard ratio (Man HoG &amp; no crisis as baseline)&quot;</span>) <span class="sc">+</span> </span>
<span id="cb295-11"><a href="#cb295-11" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb295-12"><a href="#cb295-12" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="dv">10</span>, <span class="at">xend =</span> <span class="dv">10</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb295-13"><a href="#cb295-13" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">9.5</span>, <span class="at">xend =</span> <span class="dv">10</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb295-14"><a href="#cb295-14" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">9.5</span>, <span class="at">xend =</span> <span class="dv">10</span>, <span class="at">y =</span> <span class="dv">2</span>, <span class="at">yend =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb295-15"><a href="#cb295-15" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="dv">9</span>,  <span class="at">y =</span> <span class="fl">1.5</span>, <span class="at">label =</span> <span class="st">&quot;p = 0.21&quot;</span>, <span class="at">angle =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb295-16"><a href="#cb295-16" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="dv">12</span>, <span class="at">xend =</span> <span class="dv">12</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb295-17"><a href="#cb295-17" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">11.5</span>, <span class="at">xend =</span> <span class="dv">12</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb295-18"><a href="#cb295-18" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">11.5</span>, <span class="at">xend =</span> <span class="dv">12</span>, <span class="at">y =</span> <span class="dv">3</span>, <span class="at">yend =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb295-19"><a href="#cb295-19" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="dv">11</span>,  <span class="at">y =</span> <span class="fl">2.5</span>, <span class="at">label =</span> <span class="st">&quot;p = 0.09&quot;</span>, <span class="at">angle =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb295-20"><a href="#cb295-20" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">10</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb296"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb296-1"><a href="#cb296-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureB3_WHoGcurrent.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>)</span></code></pre></div>
</div>
</div>
</div>
<div id="finance-minister-tenure" class="section level1">
<h1>3. Finance Minister Tenure</h1>
<div id="main-analysis-1" class="section level2">
<h2>Main Analysis</h2>
<div id="table-2" class="section level3">
<h3>Table 2</h3>
<p>Estimate six models</p>
<div class="sourceCode" id="cb297"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb297-1"><a href="#cb297-1" tabindex="-1"></a><span class="do">## Base model formula</span></span>
<span id="cb297-2"><a href="#cb297-2" tabindex="-1"></a>fml_tenure <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(tenure_t0, tenure_t, tenure_d) <span class="sc">~</span> . )</span>
<span id="cb297-3"><a href="#cb297-3" tabindex="-1"></a>fml_tenure_cov <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(tenure_t0, tenure_t, tenure_d) <span class="sc">~</span> womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb297-4"><a href="#cb297-4" tabindex="-1"></a>                                  presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb297-5"><a href="#cb297-5" tabindex="-1"></a>                                  growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb297-6"><a href="#cb297-6" tabindex="-1"></a>                                  tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag)</span>
<span id="cb297-7"><a href="#cb297-7" tabindex="-1"></a></span>
<span id="cb297-8"><a href="#cb297-8" tabindex="-1"></a>df_m1 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(crisis_mon))</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<div class="sourceCode" id="cb299"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb299-1"><a href="#cb299-1" tabindex="-1"></a><span class="do">## Estimate six models</span></span>
<span id="cb299-2"><a href="#cb299-2" tabindex="-1"></a>fmt_m1 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon), </span>
<span id="cb299-3"><a href="#cb299-3" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> spellid, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>))</span>
<span id="cb299-4"><a href="#cb299-4" tabindex="-1"></a>fmt_m2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> fm_gender))</span>
<span id="cb299-5"><a href="#cb299-5" tabindex="-1"></a>fmt_m3 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender))</span>
<span id="cb299-6"><a href="#cb299-6" tabindex="-1"></a>fmt_m4 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis))</span>
<span id="cb299-7"><a href="#cb299-7" tabindex="-1"></a>fmt_m5 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> .))</span>
<span id="cb299-8"><a href="#cb299-8" tabindex="-1"></a>fmt_m6 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> .))</span>
<span id="cb299-9"><a href="#cb299-9" tabindex="-1"></a></span>
<span id="cb299-10"><a href="#cb299-10" tabindex="-1"></a><span class="do">## Put them together</span></span>
<span id="cb299-11"><a href="#cb299-11" tabindex="-1"></a>model_list_tbl2 <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m1, fmt_m2, fmt_m3, fmt_m4, fmt_m5, fmt_m6)</span>
<span id="cb299-12"><a href="#cb299-12" tabindex="-1"></a><span class="do">## Add AIC</span></span>
<span id="cb299-13"><a href="#cb299-13" tabindex="-1"></a>model_list_tbl2 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tbl2, add_AIC)</span></code></pre></div>
<p>Show hazard rate coefficients.</p>
<div class="sourceCode" id="cb300"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb300-1"><a href="#cb300-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tbl2,</span>
<span id="cb300-2"><a href="#cb300-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tbl2, get_clse_hr),</span>
<span id="cb300-3"><a href="#cb300-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb300-4"><a href="#cb300-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb300-5"><a href="#cb300-5" tabindex="-1"></a>                               <span class="st">&quot;Woman Minister&quot;</span>,</span>
<span id="cb300-6"><a href="#cb300-6" tabindex="-1"></a>                               <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb300-7"><a href="#cb300-7" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb300-8"><a href="#cb300-8" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>,</span>
<span id="cb300-9"><a href="#cb300-9" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb300-10"><a href="#cb300-10" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>,</span>
<span id="cb300-11"><a href="#cb300-11" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span></span>
<span id="cb300-12"><a href="#cb300-12" tabindex="-1"></a>                               ),</span>
<span id="cb300-13"><a href="#cb300-13" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb300-14"><a href="#cb300-14" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb300-15"><a href="#cb300-15" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table 2: Risk of a Finance Minister Leaving Office, 1972-2017&quot;</span>,</span>
<span id="cb300-16"><a href="#cb300-16" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb300-17"><a href="#cb300-17" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/table2.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table 2: Risk of a Finance Minister Leaving Office,
1972-2017</strong>
</caption>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="6">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
<td>
(5)
</td>
<td>
(6)
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
0.466<sup>***</sup>
</td>
<td>
</td>
<td>
0.467<sup>***</sup>
</td>
<td>
0.480<sup>***</sup>
</td>
<td>
0.322<sup>***</sup>
</td>
<td>
0.346<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.066)
</td>
<td>
</td>
<td>
(0.066)
</td>
<td>
(0.068)
</td>
<td>
(0.079)
</td>
<td>
(0.082)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Minister
</td>
<td>
</td>
<td>
-0.093
</td>
<td>
-0.103
</td>
<td>
-0.074
</td>
<td>
0.068
</td>
<td>
0.129
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.092)
</td>
<td>
(0.091)
</td>
<td>
(0.100)
</td>
<td>
(0.111)
</td>
<td>
(0.124)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.311
</td>
<td>
</td>
<td>
-0.480<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.245)
</td>
<td>
</td>
<td>
(0.252)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.049
</td>
<td>
-0.056
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.137)
</td>
<td>
(0.138)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.118<sup>**</sup>
</td>
<td>
0.115<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.213<sup>***</sup>
</td>
<td>
0.212<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.044
</td>
<td>
-0.045
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.021<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.011
</td>
<td>
0.011
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.040
</td>
<td>
-0.039
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.017
</td>
<td>
-0.017
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
99,670
</td>
<td>
99,670
</td>
<td>
99,670
</td>
<td>
99,670
</td>
<td>
64,057
</td>
<td>
64,057
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-21,499.050
</td>
<td>
-21,519.520
</td>
<td>
-21,498.470
</td>
<td>
-21,498.050
</td>
<td>
-13,748.160
</td>
<td>
-13,747.280
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
43,000.100
</td>
<td>
43,041.050
</td>
<td>
43,000.930
</td>
<td>
43,002.100
</td>
<td>
27,520.320
</td>
<td>
27,520.550
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="6" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Calculate Ns</p>
<p>Models 1-4</p>
<div class="sourceCode" id="cb301"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb301-1"><a href="#cb301-1" tabindex="-1"></a>calc_n_1 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb301-2"><a href="#cb301-2" tabindex="-1"></a>  <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> </span>
<span id="cb301-3"><a href="#cb301-3" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb301-4"><a href="#cb301-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb301-5"><a href="#cb301-5" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens),</span>
<span id="cb301-6"><a href="#cb301-6" tabindex="-1"></a>            <span class="at">fm_gender =</span> <span class="fu">median</span>(fm_gender)</span>
<span id="cb301-7"><a href="#cb301-7" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 3,257 rows and 4 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb305"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb305-1"><a href="#cb305-1" tabindex="-1"></a><span class="fu">with</span>(calc_n_1, <span class="fu">table</span>(fail, right_cens_max))</span></code></pre></div>
<pre><code>##     right_cens_max
## fail    0    1
##    0    0  202
##    1 3055    0</code></pre>
<div class="sourceCode" id="cb307"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb307-1"><a href="#cb307-1" tabindex="-1"></a><span class="fu">with</span>(calc_n_1, <span class="fu">table</span>(fm_gender, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>## fm_gender
##    0    1 
## 3134  123</code></pre>
<p>Models 5-6</p>
<div class="sourceCode" id="cb309"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb309-1"><a href="#cb309-1" tabindex="-1"></a>calc_n_2 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb309-2"><a href="#cb309-2" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb309-3"><a href="#cb309-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(inflate_lag) <span class="sc">&amp;</span> </span>
<span id="cb309-4"><a href="#cb309-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb309-5"><a href="#cb309-5" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb309-6"><a href="#cb309-6" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb309-7"><a href="#cb309-7" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens),</span>
<span id="cb309-8"><a href="#cb309-8" tabindex="-1"></a>            <span class="at">fm_gender =</span> <span class="fu">median</span>(fm_gender)</span>
<span id="cb309-9"><a href="#cb309-9" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 36,455 rows (36%), 64,057 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,287 rows and 4 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb313"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb313-1"><a href="#cb313-1" tabindex="-1"></a><span class="fu">table</span>(calc_n_2 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  209 2078</code></pre>
<div class="sourceCode" id="cb315"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb315-1"><a href="#cb315-1" tabindex="-1"></a><span class="fu">with</span>(calc_n_2, <span class="fu">table</span>(fm_gender, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>## fm_gender
##    0    1 
## 2195   92</code></pre>
</div>
<div id="figure-3" class="section level3">
<h3>Figure 3</h3>
<p>Plot for Models 1 and 5</p>
<div class="sourceCode" id="cb317"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb317-1"><a href="#cb317-1" tabindex="-1"></a>model_list_15 <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m1, fmt_m5)</span>
<span id="cb317-2"><a href="#cb317-2" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">pe =</span> <span class="cn">NA</span>, <span class="at">low =</span> <span class="cn">NA</span>, <span class="at">up =</span> <span class="cn">NA</span>)</span>
<span id="cb317-3"><a href="#cb317-3" tabindex="-1"></a></span>
<span id="cb317-4"><a href="#cb317-4" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(model_list_15)){</span>
<span id="cb317-5"><a href="#cb317-5" tabindex="-1"></a>  df_hr[i, ] <span class="ot">&lt;-</span> <span class="fu">extract_hr</span>(model_list_15[[i]])</span>
<span id="cb317-6"><a href="#cb317-6" tabindex="-1"></a>}</span>
<span id="cb317-7"><a href="#cb317-7" tabindex="-1"></a></span>
<span id="cb317-8"><a href="#cb317-8" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> df_hr <span class="sc">|&gt;</span> </span>
<span id="cb317-9"><a href="#cb317-9" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">model =</span> <span class="fu">c</span>(<span class="st">&quot;Model 1: Bivariate</span><span class="sc">\n</span><span class="st">3,257 minister-</span><span class="sc">\n</span><span class="st">tenures&quot;</span>, </span>
<span id="cb317-10"><a href="#cb317-10" tabindex="-1"></a>                   <span class="st">&quot;Model 5: Full</span><span class="sc">\n</span><span class="st">2,287 minister-</span><span class="sc">\n</span><span class="st">tenures&quot;</span>), </span>
<span id="cb317-11"><a href="#cb317-11" tabindex="-1"></a>         <span class="at">mnum =</span> <span class="fu">seq</span>(<span class="dv">2</span>, <span class="dv">1</span>))</span></code></pre></div>
<pre><code>## mutate: new variable &#39;model&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;mnum&#39; (integer) with 2 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb320"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb320-1"><a href="#cb320-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> df_hr, </span>
<span id="cb320-2"><a href="#cb320-2" tabindex="-1"></a>       <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> pe, <span class="at">y =</span> <span class="fu">reorder</span>(model, mnum))) <span class="sc">+</span> </span>
<span id="cb320-3"><a href="#cb320-3" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="dv">2</span>, <span class="at">alpha =</span> <span class="fl">0.4</span>) <span class="sc">+</span> </span>
<span id="cb320-4"><a href="#cb320-4" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> low, <span class="at">xend =</span> up, <span class="at">yend =</span> <span class="fu">reorder</span>(model, mnum))) <span class="sc">+</span> </span>
<span id="cb320-5"><a href="#cb320-5" tabindex="-1"></a>  <span class="fu">geom_label</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">round</span>(pe, <span class="dv">2</span>))) <span class="sc">+</span> </span>
<span id="cb320-6"><a href="#cb320-6" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="fl">1.5</span>, <span class="dv">2</span>)) <span class="sc">+</span> </span>
<span id="cb320-7"><a href="#cb320-7" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Hazard ratio for banking crisis&quot;</span>, <span class="at">y =</span> <span class="cn">NULL</span>,</span>
<span id="cb320-8"><a href="#cb320-8" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span> </span>
<span id="cb320-9"><a href="#cb320-9" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb320-10"><a href="#cb320-10" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">12</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb321"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb321-1"><a href="#cb321-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/Figure3_Tenure.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>)</span></code></pre></div>
</div>
</div>
<div id="testing-non-proportionality-1" class="section level2">
<h2>Testing non-proportionality</h2>
<div id="table-b12" class="section level3">
<h3>Table B12</h3>
<p>We follow Park and Hendry’s (2015) approach to test the
non-proportionality using different transformations of time.</p>
<p>Model 1</p>
<div class="sourceCode" id="cb322"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb322-1"><a href="#cb322-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m1, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##            km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon    8.631     1 0.003      8.451       1  0.004
## GLOBAL        8.631     1 0.003      8.451       1  0.004</code></pre>
<p>Model 2</p>
<div class="sourceCode" id="cb324"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb324-1"><a href="#cb324-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m2, <span class="at">digits =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>##           km.chisq km.df km.p rank.chisq rank.df rank.p
## fm_gender     1.29     1 0.26       1.27       1   0.26
## GLOBAL        1.29     1 0.26       1.27       1   0.26</code></pre>
<p>Model 3</p>
<div class="sourceCode" id="cb326"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb326-1"><a href="#cb326-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m3, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##            km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon    8.646     1 0.003      8.463       1  0.004
## fm_gender     1.357     1 0.244      1.340       1  0.247
## GLOBAL        9.940     2 0.007      9.747       2  0.008</code></pre>
<p>Model 4</p>
<div class="sourceCode" id="cb328"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb328-1"><a href="#cb328-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m4, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##               km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon       8.667     1 0.003      8.477       1  0.004
## fm_gender        1.107     1 0.293      1.089       1  0.297
## gender_crisis    3.768     1 0.052      3.753       1  0.053
## GLOBAL          11.318     3 0.010     11.140       3  0.011</code></pre>
<p>Model 5</p>
<div class="sourceCode" id="cb330"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb330-1"><a href="#cb330-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m5, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##                   km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon           3.040     1 0.081      3.025       1  0.082
## fm_gender            1.641     1 0.200      1.632       1  0.201
## womenrep_lag         8.903     1 0.003      9.148       1  0.002
## leadgender           0.105     1 0.746      0.077       1  0.781
## presidential         0.879     1 0.349      0.966       1  0.326
## xm_qudsest           4.932     1 0.026      5.054       1  0.025
## unified_aut          0.422     1 0.516      0.361       1  0.548
## growth_lag           1.751     1 0.186      1.774       1  0.183
## inflate_lag          0.150     1 0.699      0.122       1  0.727
## loggdppc_lag         1.359     1 0.244      1.280       1  0.258
## tradepergdp_i_lag    4.320     1 0.038      4.289       1  0.038
## kaopen_i_lag         1.339     1 0.247      1.303       1  0.254
## GLOBAL              24.947    12 0.015     25.437      12  0.013</code></pre>
<p>Model 6</p>
<div class="sourceCode" id="cb332"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb332-1"><a href="#cb332-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m6, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##                   km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon           3.039     1 0.081      3.019       1  0.082
## fm_gender            1.424     1 0.233      1.412       1  0.235
## gender_crisis        1.692     1 0.193      1.689       1  0.194
## womenrep_lag         8.656     1 0.003      8.898       1  0.003
## leadgender           0.097     1 0.755      0.071       1  0.790
## presidential         0.902     1 0.342      0.991       1  0.320
## xm_qudsest           4.801     1 0.028      4.922       1  0.027
## unified_aut          0.436     1 0.509      0.374       1  0.541
## growth_lag           1.841     1 0.175      1.862       1  0.172
## inflate_lag          0.148     1 0.701      0.119       1  0.730
## loggdppc_lag         1.282     1 0.257      1.207       1  0.272
## tradepergdp_i_lag    4.334     1 0.037      4.303       1  0.038
## kaopen_i_lag         1.360     1 0.244      1.324       1  0.250
## GLOBAL              25.306    13 0.021     25.797      13  0.018</code></pre>
<p>Summary of Results</p>
<div class="sourceCode" id="cb334"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb334-1"><a href="#cb334-1" tabindex="-1"></a>zph_m1 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(fmt_m1, <span class="at">digits =</span> <span class="dv">3</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb334-2"><a href="#cb334-2" tabindex="-1"></a>zph_m2 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(fmt_m2, <span class="at">digits =</span> <span class="dv">2</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb334-3"><a href="#cb334-3" tabindex="-1"></a>zph_m3 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(fmt_m3, <span class="at">digits =</span> <span class="dv">3</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb334-4"><a href="#cb334-4" tabindex="-1"></a>zph_m4 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(fmt_m4, <span class="at">digits =</span> <span class="dv">3</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb334-5"><a href="#cb334-5" tabindex="-1"></a>zph_m5 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(fmt_m5, <span class="at">digits =</span> <span class="dv">3</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb334-6"><a href="#cb334-6" tabindex="-1"></a>zph_m6 <span class="ot">&lt;-</span> <span class="fu">zphTable</span>(fmt_m6, <span class="at">digits =</span> <span class="dv">3</span>)[, <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">6</span>)]</span>
<span id="cb334-7"><a href="#cb334-7" tabindex="-1"></a></span>
<span id="cb334-8"><a href="#cb334-8" tabindex="-1"></a>zphdf_m1 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m1), <span class="at">km.p1 =</span> zph_m1[,<span class="dv">1</span>], <span class="at">rank.p1 =</span> zph_m1[,<span class="dv">2</span>])</span>
<span id="cb334-9"><a href="#cb334-9" tabindex="-1"></a>zphdf_m2 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m2), <span class="at">km.p2 =</span> zph_m2[,<span class="dv">1</span>], <span class="at">rank.p2 =</span> zph_m2[,<span class="dv">2</span>])</span>
<span id="cb334-10"><a href="#cb334-10" tabindex="-1"></a>zphdf_m3 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m3), <span class="at">km.p3 =</span> zph_m3[,<span class="dv">1</span>], <span class="at">rank.p3 =</span> zph_m3[,<span class="dv">2</span>])</span>
<span id="cb334-11"><a href="#cb334-11" tabindex="-1"></a>zphdf_m4 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m4), <span class="at">km.p4 =</span> zph_m4[,<span class="dv">1</span>], <span class="at">rank.p4 =</span> zph_m4[,<span class="dv">2</span>])</span>
<span id="cb334-12"><a href="#cb334-12" tabindex="-1"></a>zphdf_m5 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m5), <span class="at">km.p5 =</span> zph_m5[,<span class="dv">1</span>], <span class="at">rank.p5 =</span> zph_m5[,<span class="dv">2</span>])</span>
<span id="cb334-13"><a href="#cb334-13" tabindex="-1"></a>zphdf_m6 <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rownames</span>(zph_m6), <span class="at">km.p6 =</span> zph_m6[,<span class="dv">1</span>], <span class="at">rank.p6 =</span> zph_m6[,<span class="dv">2</span>])</span>
<span id="cb334-14"><a href="#cb334-14" tabindex="-1"></a></span>
<span id="cb334-15"><a href="#cb334-15" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_m1, zphdf_m2)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p2, rank.p2)
## &gt; rows only in x 1
## &gt; rows only in y 1
## &gt; matched rows 1
## &gt; ===
## &gt; rows total 3</code></pre>
<div class="sourceCode" id="cb336"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb336-1"><a href="#cb336-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m3)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p3, rank.p3)
## &gt; rows only in x 0
## &gt; rows only in y 0
## &gt; matched rows 3
## &gt; ===
## &gt; rows total 3</code></pre>
<div class="sourceCode" id="cb338"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb338-1"><a href="#cb338-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m4)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p4, rank.p4)
## &gt; rows only in x 0
## &gt; rows only in y 1
## &gt; matched rows 3
## &gt; ===
## &gt; rows total 4</code></pre>
<div class="sourceCode" id="cb340"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb340-1"><a href="#cb340-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m5)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p5, rank.p5)
## &gt; rows only in x 1
## &gt; rows only in y 10
## &gt; matched rows 3
## &gt; ====
## &gt; rows total 14</code></pre>
<div class="sourceCode" id="cb342"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb342-1"><a href="#cb342-1" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">full_join</span>(zphdf_sum, zphdf_m6)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(x)`
## full_join: added 2 columns (km.p6, rank.p6)
## &gt; rows only in x 0
## &gt; rows only in y 0
## &gt; matched rows 14
## &gt; ====
## &gt; rows total 14</code></pre>
<div class="sourceCode" id="cb344"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb344-1"><a href="#cb344-1" tabindex="-1"></a>xid <span class="ot">&lt;-</span> <span class="fu">which</span>(zphdf_sum <span class="sc">$</span> x <span class="sc">==</span> <span class="st">&quot;GLOBAL&quot;</span>)</span>
<span id="cb344-2"><a href="#cb344-2" tabindex="-1"></a>zphdf_sum <span class="ot">&lt;-</span> <span class="fu">rbind</span>(zphdf_sum[<span class="sc">-</span>xid, ], zphdf_sum[xid, ])</span>
<span id="cb344-3"><a href="#cb344-3" tabindex="-1"></a>zphdf_sum <span class="sc">$</span> x <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb344-4"><a href="#cb344-4" tabindex="-1"></a>                   <span class="st">&quot;Woman Minister&quot;</span>,</span>
<span id="cb344-5"><a href="#cb344-5" tabindex="-1"></a>                   <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb344-6"><a href="#cb344-6" tabindex="-1"></a>                   <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb344-7"><a href="#cb344-7" tabindex="-1"></a>                   <span class="st">&quot;Woman HoG&quot;</span>, <span class="st">&quot;Presidential&quot;</span>, </span>
<span id="cb344-8"><a href="#cb344-8" tabindex="-1"></a>                   <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb344-9"><a href="#cb344-9" tabindex="-1"></a>                   <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>, </span>
<span id="cb344-10"><a href="#cb344-10" tabindex="-1"></a>                   <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>, <span class="st">&quot;Global&quot;</span>)</span></code></pre></div>
<p>Summary table</p>
<div class="sourceCode" id="cb345"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb345-1"><a href="#cb345-1" tabindex="-1"></a><span class="fu">kable</span>(zphdf_sum, <span class="at">row.names =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb345-2"><a href="#cb345-2" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb345-3"><a href="#cb345-3" tabindex="-1"></a>  <span class="fu">add_header_above</span>(<span class="fu">c</span>(<span class="st">&quot; &quot;</span> <span class="ot">=</span> <span class="dv">1</span>, <span class="st">&quot;Model 1&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 2&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 3&quot;</span> <span class="ot">=</span> <span class="dv">2</span>,</span>
<span id="cb345-4"><a href="#cb345-4" tabindex="-1"></a>                     <span class="st">&quot;Model 4&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 5&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 6&quot;</span> <span class="ot">=</span> <span class="dv">2</span>))</span></code></pre></div>
<table class=" lightable-classic" style="font-family: &quot;Arial Narrow&quot;, &quot;Source Sans Pro&quot;, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="empty-cells: hide;" colspan="1">
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 1
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 2
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 3
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 4
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 5
</div>
</th>
<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #111111; margin-bottom: -1px; ">
Model 6
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
x
</th>
<th style="text-align:right;">
km.p1
</th>
<th style="text-align:right;">
rank.p1
</th>
<th style="text-align:right;">
km.p2
</th>
<th style="text-align:right;">
rank.p2
</th>
<th style="text-align:right;">
km.p3
</th>
<th style="text-align:right;">
rank.p3
</th>
<th style="text-align:right;">
km.p4
</th>
<th style="text-align:right;">
rank.p4
</th>
<th style="text-align:right;">
km.p5
</th>
<th style="text-align:right;">
rank.p5
</th>
<th style="text-align:right;">
km.p6
</th>
<th style="text-align:right;">
rank.p6
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Banking Crisis
</td>
<td style="text-align:right;">
0.003
</td>
<td style="text-align:right;">
0.004
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.003
</td>
<td style="text-align:right;">
0.004
</td>
<td style="text-align:right;">
0.003
</td>
<td style="text-align:right;">
0.004
</td>
<td style="text-align:right;">
0.081
</td>
<td style="text-align:right;">
0.082
</td>
<td style="text-align:right;">
0.081
</td>
<td style="text-align:right;">
0.082
</td>
</tr>
<tr>
<td style="text-align:left;">
Woman Minister
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.26
</td>
<td style="text-align:right;">
0.26
</td>
<td style="text-align:right;">
0.244
</td>
<td style="text-align:right;">
0.247
</td>
<td style="text-align:right;">
0.293
</td>
<td style="text-align:right;">
0.297
</td>
<td style="text-align:right;">
0.200
</td>
<td style="text-align:right;">
0.201
</td>
<td style="text-align:right;">
0.233
</td>
<td style="text-align:right;">
0.235
</td>
</tr>
<tr>
<td style="text-align:left;">
Crisis x Woman
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.052
</td>
<td style="text-align:right;">
0.053
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.193
</td>
<td style="text-align:right;">
0.194
</td>
</tr>
<tr>
<td style="text-align:left;">
Women in Parliament
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.003
</td>
<td style="text-align:right;">
0.002
</td>
<td style="text-align:right;">
0.003
</td>
<td style="text-align:right;">
0.003
</td>
</tr>
<tr>
<td style="text-align:left;">
Woman HoG
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.746
</td>
<td style="text-align:right;">
0.781
</td>
<td style="text-align:right;">
0.755
</td>
<td style="text-align:right;">
0.790
</td>
</tr>
<tr>
<td style="text-align:left;">
Presidential
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.349
</td>
<td style="text-align:right;">
0.326
</td>
<td style="text-align:right;">
0.342
</td>
<td style="text-align:right;">
0.320
</td>
</tr>
<tr>
<td style="text-align:left;">
Democracy Score
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.026
</td>
<td style="text-align:right;">
0.025
</td>
<td style="text-align:right;">
0.028
</td>
<td style="text-align:right;">
0.027
</td>
</tr>
<tr>
<td style="text-align:left;">
Unified Government
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.516
</td>
<td style="text-align:right;">
0.548
</td>
<td style="text-align:right;">
0.509
</td>
<td style="text-align:right;">
0.541
</td>
</tr>
<tr>
<td style="text-align:left;">
Economic Growth
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.186
</td>
<td style="text-align:right;">
0.183
</td>
<td style="text-align:right;">
0.175
</td>
<td style="text-align:right;">
0.172
</td>
</tr>
<tr>
<td style="text-align:left;">
Inflation
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.699
</td>
<td style="text-align:right;">
0.727
</td>
<td style="text-align:right;">
0.701
</td>
<td style="text-align:right;">
0.730
</td>
</tr>
<tr>
<td style="text-align:left;">
GDP per Capita
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.244
</td>
<td style="text-align:right;">
0.258
</td>
<td style="text-align:right;">
0.257
</td>
<td style="text-align:right;">
0.272
</td>
</tr>
<tr>
<td style="text-align:left;">
Trade Openness
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.038
</td>
<td style="text-align:right;">
0.038
</td>
<td style="text-align:right;">
0.037
</td>
<td style="text-align:right;">
0.038
</td>
</tr>
<tr>
<td style="text-align:left;">
Capital Openness
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
NA
</td>
<td style="text-align:right;">
0.247
</td>
<td style="text-align:right;">
0.254
</td>
<td style="text-align:right;">
0.244
</td>
<td style="text-align:right;">
0.250
</td>
</tr>
<tr>
<td style="text-align:left;">
Global
</td>
<td style="text-align:right;">
0.003
</td>
<td style="text-align:right;">
0.004
</td>
<td style="text-align:right;">
0.26
</td>
<td style="text-align:right;">
0.26
</td>
<td style="text-align:right;">
0.007
</td>
<td style="text-align:right;">
0.008
</td>
<td style="text-align:right;">
0.010
</td>
<td style="text-align:right;">
0.011
</td>
<td style="text-align:right;">
0.015
</td>
<td style="text-align:right;">
0.013
</td>
<td style="text-align:right;">
0.021
</td>
<td style="text-align:right;">
0.018
</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb346"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb346-1"><a href="#cb346-1" tabindex="-1"></a><span class="fu">kable</span>(zphdf_sum, <span class="at">row.names =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb346-2"><a href="#cb346-2" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span> </span>
<span id="cb346-3"><a href="#cb346-3" tabindex="-1"></a>  <span class="fu">add_header_above</span>(<span class="fu">c</span>(<span class="st">&quot; &quot;</span> <span class="ot">=</span> <span class="dv">1</span>, <span class="st">&quot;Model 1&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 2&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 3&quot;</span> <span class="ot">=</span> <span class="dv">2</span>,</span>
<span id="cb346-4"><a href="#cb346-4" tabindex="-1"></a>                     <span class="st">&quot;Model 4&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 5&quot;</span> <span class="ot">=</span> <span class="dv">2</span>, <span class="st">&quot;Model 6&quot;</span> <span class="ot">=</span> <span class="dv">2</span>)) <span class="sc">|&gt;</span> </span>
<span id="cb346-5"><a href="#cb346-5" tabindex="-1"></a>  <span class="fu">save_kable</span>(<span class="at">file =</span> <span class="st">&quot;Tables/tableB12.html&quot;</span>, <span class="at">self_contained =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<p>Global test suggest that Models 1, 3, 4, 5, and 6 exhibits potential
violations of the PH assumption. We thus estimate a series of models
that relax the PH assumption.</p>
<p>Model 1</p>
<div class="sourceCode" id="cb347"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb347-1"><a href="#cb347-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m1, <span class="at">digits =</span> <span class="dv">3</span>)</span></code></pre></div>
<pre><code>##            km.chisq km.df  km.p rank.chisq rank.df rank.p
## crisis_mon    8.631     1 0.003      8.451       1  0.004
## GLOBAL        8.631     1 0.003      8.451       1  0.004</code></pre>
<p>Estimate models that include an interaction term between a function
of time, <span class="math inline">\(f(t)\)</span>, and the offending
variable. We use three functional forms for <span class="math inline">\(f(t)\)</span> (linear time, log time, and square
root of time), and choose the functional form that produces the best
model fit.</p>
<div class="sourceCode" id="cb349"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb349-1"><a href="#cb349-1" tabindex="-1"></a><span class="do">## t</span></span>
<span id="cb349-2"><a href="#cb349-2" tabindex="-1"></a>fmt_m1nph1 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> <span class="fu">tt</span>(crisis_mon)), </span>
<span id="cb349-3"><a href="#cb349-3" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> spellid, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>), </span>
<span id="cb349-4"><a href="#cb349-4" tabindex="-1"></a>                <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb349-5"><a href="#cb349-5" tabindex="-1"></a><span class="do">## log(t)</span></span>
<span id="cb349-6"><a href="#cb349-6" tabindex="-1"></a>fmt_m1nph2 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> <span class="fu">tt</span>(crisis_mon)), </span>
<span id="cb349-7"><a href="#cb349-7" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> spellid, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>), </span>
<span id="cb349-8"><a href="#cb349-8" tabindex="-1"></a>                <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb349-9"><a href="#cb349-9" tabindex="-1"></a><span class="do">## sqrt(t)</span></span>
<span id="cb349-10"><a href="#cb349-10" tabindex="-1"></a>fmt_m1nph3 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(<span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> <span class="fu">tt</span>(crisis_mon)), </span>
<span id="cb349-11"><a href="#cb349-11" tabindex="-1"></a>                <span class="at">data =</span> df, <span class="at">cluster =</span> spellid, <span class="at">ties =</span> <span class="fu">c</span>(<span class="st">&quot;efron&quot;</span>), </span>
<span id="cb349-12"><a href="#cb349-12" tabindex="-1"></a>                <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb349-13"><a href="#cb349-13" tabindex="-1"></a></span>
<span id="cb349-14"><a href="#cb349-14" tabindex="-1"></a>model_list_m1nph <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m1, fmt_m1nph1, fmt_m1nph2, fmt_m1nph3)</span>
<span id="cb349-15"><a href="#cb349-15" tabindex="-1"></a>model_list_m1nph <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_m1nph, add_AIC)</span></code></pre></div>
<p>Comparing AIC, we conclude that square root of time produces the best
model fit.</p>
<p>Model 2</p>
<div class="sourceCode" id="cb350"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb350-1"><a href="#cb350-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m2, <span class="at">digits =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>##           km.chisq km.df km.p rank.chisq rank.df rank.p
## fm_gender     1.29     1 0.26       1.27       1   0.26
## GLOBAL        1.29     1 0.26       1.27       1   0.26</code></pre>
<p>Model 3</p>
<div class="sourceCode" id="cb352"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb352-1"><a href="#cb352-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m3, <span class="at">digits =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>##            km.chisq km.df km.p rank.chisq rank.df rank.p
## crisis_mon     8.65     1 0.00       8.46       1   0.00
## fm_gender      1.36     1 0.24       1.34       1   0.25
## GLOBAL         9.94     2 0.01       9.75       2   0.01</code></pre>
<div class="sourceCode" id="cb354"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb354-1"><a href="#cb354-1" tabindex="-1"></a><span class="do">## t</span></span>
<span id="cb354-2"><a href="#cb354-2" tabindex="-1"></a>fmt_m3nph1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, </span>
<span id="cb354-3"><a href="#cb354-3" tabindex="-1"></a>                    <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> <span class="fu">tt</span>(crisis_mon) <span class="sc">+</span> fm_gender),</span>
<span id="cb354-4"><a href="#cb354-4" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb354-5"><a href="#cb354-5" tabindex="-1"></a><span class="do">## log(t)</span></span>
<span id="cb354-6"><a href="#cb354-6" tabindex="-1"></a>fmt_m3nph2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, </span>
<span id="cb354-7"><a href="#cb354-7" tabindex="-1"></a>                    <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> <span class="fu">tt</span>(crisis_mon) <span class="sc">+</span> fm_gender),</span>
<span id="cb354-8"><a href="#cb354-8" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb354-9"><a href="#cb354-9" tabindex="-1"></a><span class="do">## sqrt(t)</span></span>
<span id="cb354-10"><a href="#cb354-10" tabindex="-1"></a>fmt_m3nph3 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, </span>
<span id="cb354-11"><a href="#cb354-11" tabindex="-1"></a>                    <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> <span class="fu">tt</span>(crisis_mon) <span class="sc">+</span> fm_gender),</span>
<span id="cb354-12"><a href="#cb354-12" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb354-13"><a href="#cb354-13" tabindex="-1"></a></span>
<span id="cb354-14"><a href="#cb354-14" tabindex="-1"></a>model_list_m3nph <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m3, fmt_m3nph1, fmt_m3nph2, fmt_m3nph3)</span>
<span id="cb354-15"><a href="#cb354-15" tabindex="-1"></a>model_list_m3nph <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_m3nph, add_AIC)</span></code></pre></div>
<p>Comparing AIC, we conclude that square root of time produces the best
model fit.</p>
<p>Model 4</p>
<div class="sourceCode" id="cb355"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb355-1"><a href="#cb355-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m4, <span class="at">digits =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>##               km.chisq km.df km.p rank.chisq rank.df rank.p
## crisis_mon        8.67     1 0.00       8.48       1   0.00
## fm_gender         1.11     1 0.29       1.09       1   0.30
## gender_crisis     3.77     1 0.05       3.75       1   0.05
## GLOBAL           11.32     3 0.01      11.14       3   0.01</code></pre>
<div class="sourceCode" id="cb357"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb357-1"><a href="#cb357-1" tabindex="-1"></a>fmt_m4nph1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> </span>
<span id="cb357-2"><a href="#cb357-2" tabindex="-1"></a>                                     <span class="fu">tt</span>(crisis_mon)),</span>
<span id="cb357-3"><a href="#cb357-3" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb357-4"><a href="#cb357-4" tabindex="-1"></a>fmt_m4nph2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> </span>
<span id="cb357-5"><a href="#cb357-5" tabindex="-1"></a>                                     <span class="fu">tt</span>(crisis_mon)),</span>
<span id="cb357-6"><a href="#cb357-6" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb357-7"><a href="#cb357-7" tabindex="-1"></a>fmt_m4nph3 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> </span>
<span id="cb357-8"><a href="#cb357-8" tabindex="-1"></a>                                     <span class="fu">tt</span>(crisis_mon)),</span>
<span id="cb357-9"><a href="#cb357-9" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb357-10"><a href="#cb357-10" tabindex="-1"></a></span>
<span id="cb357-11"><a href="#cb357-11" tabindex="-1"></a>model_list_m4nph <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m4, fmt_m4nph1, fmt_m4nph2, fmt_m4nph3)</span>
<span id="cb357-12"><a href="#cb357-12" tabindex="-1"></a>model_list_m4nph <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_m4nph, add_AIC)</span></code></pre></div>
<p>Comparing AIC, we conclude that square root of time produces the best
model fit.</p>
<p>Model 5</p>
<div class="sourceCode" id="cb358"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb358-1"><a href="#cb358-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m5, <span class="at">digits =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>##                   km.chisq km.df km.p rank.chisq rank.df rank.p
## crisis_mon            3.04     1 0.08       3.02       1   0.08
## fm_gender             1.64     1 0.20       1.63       1   0.20
## womenrep_lag          8.90     1 0.00       9.15       1   0.00
## leadgender            0.10     1 0.75       0.08       1   0.78
## presidential          0.88     1 0.35       0.97       1   0.33
## xm_qudsest            4.93     1 0.03       5.05       1   0.02
## unified_aut           0.42     1 0.52       0.36       1   0.55
## growth_lag            1.75     1 0.19       1.77       1   0.18
## inflate_lag           0.15     1 0.70       0.12       1   0.73
## loggdppc_lag          1.36     1 0.24       1.28       1   0.26
## tradepergdp_i_lag     4.32     1 0.04       4.29       1   0.04
## kaopen_i_lag          1.34     1 0.25       1.30       1   0.25
## GLOBAL               24.95    12 0.02      25.44      12   0.01</code></pre>
<div class="sourceCode" id="cb360"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb360-1"><a href="#cb360-1" tabindex="-1"></a>fmt_m5nph1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> . <span class="sc">+</span> </span>
<span id="cb360-2"><a href="#cb360-2" tabindex="-1"></a>                                     <span class="fu">tt</span>(womenrep_lag) <span class="sc">+</span> <span class="fu">tt</span>(xm_qudsest) <span class="sc">+</span> </span>
<span id="cb360-3"><a href="#cb360-3" tabindex="-1"></a>                                     <span class="fu">tt</span>(tradepergdp_i_lag)),</span>
<span id="cb360-4"><a href="#cb360-4" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb360-5"><a href="#cb360-5" tabindex="-1"></a>fmt_m5nph2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> . <span class="sc">+</span> </span>
<span id="cb360-6"><a href="#cb360-6" tabindex="-1"></a>                                     <span class="fu">tt</span>(womenrep_lag) <span class="sc">+</span> <span class="fu">tt</span>(xm_qudsest) <span class="sc">+</span> </span>
<span id="cb360-7"><a href="#cb360-7" tabindex="-1"></a>                                     <span class="fu">tt</span>(tradepergdp_i_lag)),</span>
<span id="cb360-8"><a href="#cb360-8" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb360-9"><a href="#cb360-9" tabindex="-1"></a>fmt_m5nph3 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> . <span class="sc">+</span> </span>
<span id="cb360-10"><a href="#cb360-10" tabindex="-1"></a>                                     <span class="fu">tt</span>(womenrep_lag) <span class="sc">+</span> <span class="fu">tt</span>(xm_qudsest) <span class="sc">+</span> </span>
<span id="cb360-11"><a href="#cb360-11" tabindex="-1"></a>                                     <span class="fu">tt</span>(tradepergdp_i_lag)),</span>
<span id="cb360-12"><a href="#cb360-12" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb360-13"><a href="#cb360-13" tabindex="-1"></a>model_list_m5nph <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m5, fmt_m5nph1, fmt_m5nph2, fmt_m5nph3)</span>
<span id="cb360-14"><a href="#cb360-14" tabindex="-1"></a>model_list_m5nph <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_m5nph, add_AIC)</span></code></pre></div>
<p>Comparing AIC, we conclude that linear time produces the best model
fit.</p>
<p>Model 6</p>
<div class="sourceCode" id="cb361"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb361-1"><a href="#cb361-1" tabindex="-1"></a><span class="fu">zphTable</span>(fmt_m6, <span class="at">digits =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>##                   km.chisq km.df km.p rank.chisq rank.df rank.p
## crisis_mon            3.04     1 0.08       3.02       1   0.08
## fm_gender             1.42     1 0.23       1.41       1   0.23
## gender_crisis         1.69     1 0.19       1.69       1   0.19
## womenrep_lag          8.66     1 0.00       8.90       1   0.00
## leadgender            0.10     1 0.75       0.07       1   0.79
## presidential          0.90     1 0.34       0.99       1   0.32
## xm_qudsest            4.80     1 0.03       4.92       1   0.03
## unified_aut           0.44     1 0.51       0.37       1   0.54
## growth_lag            1.84     1 0.17       1.86       1   0.17
## inflate_lag           0.15     1 0.70       0.12       1   0.73
## loggdppc_lag          1.28     1 0.26       1.21       1   0.27
## tradepergdp_i_lag     4.33     1 0.04       4.30       1   0.04
## kaopen_i_lag          1.36     1 0.24       1.32       1   0.25
## GLOBAL               25.31    13 0.02      25.80      13   0.02</code></pre>
<div class="sourceCode" id="cb363"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb363-1"><a href="#cb363-1" tabindex="-1"></a>fmt_m6nph1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> . <span class="sc">+</span> </span>
<span id="cb363-2"><a href="#cb363-2" tabindex="-1"></a>                                     <span class="fu">tt</span>(womenrep_lag) <span class="sc">+</span> <span class="fu">tt</span>(xm_qudsest) <span class="sc">+</span> </span>
<span id="cb363-3"><a href="#cb363-3" tabindex="-1"></a>                                     <span class="fu">tt</span>(tradepergdp_i_lag)),</span>
<span id="cb363-4"><a href="#cb363-4" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> t)</span>
<span id="cb363-5"><a href="#cb363-5" tabindex="-1"></a>fmt_m6nph2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> . <span class="sc">+</span> </span>
<span id="cb363-6"><a href="#cb363-6" tabindex="-1"></a>                                     <span class="fu">tt</span>(womenrep_lag) <span class="sc">+</span> <span class="fu">tt</span>(xm_qudsest) <span class="sc">+</span> </span>
<span id="cb363-7"><a href="#cb363-7" tabindex="-1"></a>                                     <span class="fu">tt</span>(tradepergdp_i_lag)),</span>
<span id="cb363-8"><a href="#cb363-8" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">log</span>(t))</span>
<span id="cb363-9"><a href="#cb363-9" tabindex="-1"></a>fmt_m6nph3 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> gender_crisis <span class="sc">+</span> . <span class="sc">+</span> </span>
<span id="cb363-10"><a href="#cb363-10" tabindex="-1"></a>                                     <span class="fu">tt</span>(womenrep_lag) <span class="sc">+</span> <span class="fu">tt</span>(xm_qudsest) <span class="sc">+</span> </span>
<span id="cb363-11"><a href="#cb363-11" tabindex="-1"></a>                                     <span class="fu">tt</span>(tradepergdp_i_lag)),</span>
<span id="cb363-12"><a href="#cb363-12" tabindex="-1"></a>                    <span class="at">tt =</span> <span class="cf">function</span>(x, t, ...) x <span class="sc">*</span> <span class="fu">sqrt</span>(t))</span>
<span id="cb363-13"><a href="#cb363-13" tabindex="-1"></a></span>
<span id="cb363-14"><a href="#cb363-14" tabindex="-1"></a>model_list_m6nph <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m6, fmt_m6nph1, fmt_m6nph2, fmt_m6nph3)</span>
<span id="cb363-15"><a href="#cb363-15" tabindex="-1"></a>model_list_m6nph <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_m6nph, add_AIC)</span></code></pre></div>
<p>Comparing AIC, we conclude that linear time produces the best model
fit.</p>
<p>Summarize the results</p>
<div class="sourceCode" id="cb364"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb364-1"><a href="#cb364-1" tabindex="-1"></a>model_list_nph <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m1nph3, fmt_m3nph3, fmt_m4nph3, fmt_m5nph1, fmt_m6nph1)</span>
<span id="cb364-2"><a href="#cb364-2" tabindex="-1"></a>model_list_nph <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_nph, add_AIC)</span></code></pre></div>
<div class="sourceCode" id="cb365"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb365-1"><a href="#cb365-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_nph,</span>
<span id="cb365-2"><a href="#cb365-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_nph, get_clse_hr),</span>
<span id="cb365-3"><a href="#cb365-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb365-4"><a href="#cb365-4" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb365-5"><a href="#cb365-5" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb365-6"><a href="#cb365-6" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B13: Tenure Models: Relaxing PH Assumption &quot;</span>,</span>
<span id="cb365-7"><a href="#cb365-7" tabindex="-1"></a>          <span class="at">order =</span> <span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">6</span><span class="sc">:</span><span class="dv">8</span>, <span class="dv">16</span>, <span class="dv">9</span><span class="sc">:</span><span class="dv">13</span>, <span class="dv">17</span>, <span class="dv">14</span>),</span>
<span id="cb365-8"><a href="#cb365-8" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, <span class="st">&quot;Banking Crisis x f(time)&quot;</span>,</span>
<span id="cb365-9"><a href="#cb365-9" tabindex="-1"></a>                               <span class="st">&quot;Woman Minister&quot;</span>, <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb365-10"><a href="#cb365-10" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,<span class="st">&quot;Women in Parliament x f(time)&quot;</span>,</span>
<span id="cb365-11"><a href="#cb365-11" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>,</span>
<span id="cb365-12"><a href="#cb365-12" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Democracy Score x f(time)&quot;</span>,</span>
<span id="cb365-13"><a href="#cb365-13" tabindex="-1"></a>                               <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb365-14"><a href="#cb365-14" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>,</span>
<span id="cb365-15"><a href="#cb365-15" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Trade Openness x f(time)&quot;</span>, </span>
<span id="cb365-16"><a href="#cb365-16" tabindex="-1"></a>                               <span class="st">&quot;Capital Openness&quot;</span></span>
<span id="cb365-17"><a href="#cb365-17" tabindex="-1"></a>                               ),</span>
<span id="cb365-18"><a href="#cb365-18" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>, </span>
<span id="cb365-19"><a href="#cb365-19" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB13.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B13: Tenure Models: Relaxing PH Assumption</strong>
</caption>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="5">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
<td>
(5)
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
0.088
</td>
<td>
0.090
</td>
<td>
0.104
</td>
<td>
0.325<sup>***</sup>
</td>
<td>
0.347<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.145)
</td>
<td>
(0.145)
</td>
<td>
(0.146)
</td>
<td>
(0.079)
</td>
<td>
(0.082)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis x f(time)
</td>
<td>
0.090<sup>***</sup>
</td>
<td>
0.090<sup>***</sup>
</td>
<td>
0.089<sup>***</sup>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.028)
</td>
<td>
(0.028)
</td>
<td>
(0.028)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Minister
</td>
<td>
</td>
<td>
-0.102
</td>
<td>
-0.074
</td>
<td>
0.090
</td>
<td>
0.146
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.090)
</td>
<td>
(0.099)
</td>
<td>
(0.116)
</td>
<td>
(0.130)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
</td>
<td>
</td>
<td>
-0.300
</td>
<td>
</td>
<td>
-0.448<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.242)
</td>
<td>
</td>
<td>
(0.253)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.022<sup>***</sup>
</td>
<td>
-0.022<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.004)
</td>
<td>
(0.004)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament x f(time)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.0002<sup>***</sup>
</td>
<td>
0.0002<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.0001)
</td>
<td>
(0.0001)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.038
</td>
<td>
-0.044
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.139)
</td>
<td>
(0.140)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.113<sup>**</sup>
</td>
<td>
0.111<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.050)
</td>
<td>
(0.050)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.151<sup>***</sup>
</td>
<td>
0.152<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.047)
</td>
<td>
(0.047)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score x f(time)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.001<sup>*</sup>
</td>
<td>
0.001<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.001)
</td>
<td>
(0.001)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.045
</td>
<td>
-0.047
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.047)
</td>
<td>
(0.047)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.020<sup>***</sup>
</td>
<td>
-0.020<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.010
</td>
<td>
0.010
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.030
</td>
<td>
-0.029
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.026)
</td>
<td>
(0.026)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.004<sup>***</sup>
</td>
<td>
-0.004<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.001)
</td>
<td>
(0.001)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness x f(time)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.00003<sup>***</sup>
</td>
<td>
0.00003<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.00001)
</td>
<td>
(0.00001)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
-0.015
</td>
<td>
-0.016
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
99,670
</td>
<td>
99,670
</td>
<td>
99,670
</td>
<td>
64,057
</td>
<td>
64,057
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-21,494.890
</td>
<td>
-21,494.320
</td>
<td>
-21,493.940
</td>
<td>
-13,738.200
</td>
<td>
-13,737.430
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
42,993.780
</td>
<td>
42,994.640
</td>
<td>
42,995.870
</td>
<td>
27,506.400
</td>
<td>
27,506.870
</td>
</tr>
<tr>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="5" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
</div>
<div id="figure-b4" class="section level3">
<h3>Figure B4</h3>
<p>We visualize the effects of crisis on tenure over time for Models 1
and 4.</p>
<p>Model 1</p>
<div class="sourceCode" id="cb366"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb366-1"><a href="#cb366-1" tabindex="-1"></a>est_m1_3 <span class="ot">&lt;-</span> <span class="fu">calcNPH</span>(<span class="at">fit =</span> fmt_m1nph3, <span class="at">tfunc =</span> <span class="st">&quot;sqrt&quot;</span>)</span>
<span id="cb366-2"><a href="#cb366-2" tabindex="-1"></a></span>
<span id="cb366-3"><a href="#cb366-3" tabindex="-1"></a>gm1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> est_m1_3, <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> time)) <span class="sc">+</span> </span>
<span id="cb366-4"><a href="#cb366-4" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>) <span class="sc">+</span> </span>
<span id="cb366-5"><a href="#cb366-5" tabindex="-1"></a>  <span class="fu">geom_ribbon</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> low95, <span class="at">ymax =</span> upp95), <span class="at">alpha =</span> <span class="fl">0.5</span>) <span class="sc">+</span> </span>
<span id="cb366-6"><a href="#cb366-6" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> median)) <span class="sc">+</span> </span>
<span id="cb366-7"><a href="#cb366-7" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>(<span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">4</span>, <span class="dv">8</span>, <span class="dv">16</span>, <span class="dv">32</span>, <span class="dv">64</span>, <span class="dv">128</span>)) <span class="sc">+</span> </span>
<span id="cb366-8"><a href="#cb366-8" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Time since appointment (months)&quot;</span>, </span>
<span id="cb366-9"><a href="#cb366-9" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Hazard ratio for banking crisis&quot;</span>) <span class="sc">+</span> </span>
<span id="cb366-10"><a href="#cb366-10" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb366-11"><a href="#cb366-11" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>), </span>
<span id="cb366-12"><a href="#cb366-12" tabindex="-1"></a>        <span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>)</span>
<span id="cb366-13"><a href="#cb366-13" tabindex="-1"></a>        ) </span>
<span id="cb366-14"><a href="#cb366-14" tabindex="-1"></a></span>
<span id="cb366-15"><a href="#cb366-15" tabindex="-1"></a>gm1_hist <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_d <span class="sc">==</span> <span class="dv">1</span>)) <span class="sc">+</span> </span>
<span id="cb366-16"><a href="#cb366-16" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="fu">aes</span>(<span class="at">x =</span> tenure_t), <span class="at">color =</span> <span class="st">&quot;black&quot;</span>, <span class="at">fill =</span> <span class="st">&quot;white&quot;</span>) <span class="sc">+</span> </span>
<span id="cb366-17"><a href="#cb366-17" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>() <span class="sc">+</span> </span>
<span id="cb366-18"><a href="#cb366-18" tabindex="-1"></a>  <span class="fu">theme_void</span>()</span></code></pre></div>
<pre><code>## filter: removed 97,457 rows (97%), 3,055 rows remaining</code></pre>
<div class="sourceCode" id="cb368"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb368-1"><a href="#cb368-1" tabindex="-1"></a>gm1_hist <span class="sc">+</span> </span>
<span id="cb368-2"><a href="#cb368-2" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Model 1&quot;</span>) <span class="sc">+</span> </span>
<span id="cb368-3"><a href="#cb368-3" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">18</span>)) <span class="sc">+</span> </span>
<span id="cb368-4"><a href="#cb368-4" tabindex="-1"></a>  gm1 <span class="sc">+</span> </span>
<span id="cb368-5"><a href="#cb368-5" tabindex="-1"></a>  <span class="fu">plot_layout</span>(</span>
<span id="cb368-6"><a href="#cb368-6" tabindex="-1"></a>    <span class="at">ncol =</span> <span class="dv">1</span>, </span>
<span id="cb368-7"><a href="#cb368-7" tabindex="-1"></a>    <span class="at">nrow =</span> <span class="dv">2</span>, </span>
<span id="cb368-8"><a href="#cb368-8" tabindex="-1"></a>    <span class="at">widths =</span> <span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">1</span>),</span>
<span id="cb368-9"><a href="#cb368-9" tabindex="-1"></a>    <span class="at">heights =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">4</span>)</span>
<span id="cb368-10"><a href="#cb368-10" tabindex="-1"></a>  )</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAYAAAD0ZtPZAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAVAoAMABAAAAAEAAAPAAAAAALYRw1EAAEAASURBVHgB7N0J3JzTvTjw8yaRFYmIRC4JUopyKVJae5FSay3R1FZK9VKq/dhaS1tLXVVXUbT3Um5t11Z7q6GWCIk1aCRIkIglq0T2RZZ/z9w785933nnzvjPvzDvPzHzP5/OaZznPec75nudNJj/nPKdh1T9TkAgQIECAAAECBAgQIECAAAECBAgQIFCDAh1qsE2aRIAAAQIECBAgQIAAAQIECBAgQIAAgZSAAKgHgQABAgQIECBAgAABAgQIECBAgACBmhUQAK3ZrtUwAgQIECBAgAABAgQIECBAgAABAgQEQD0DBAgQIECAAAECBAgQIECAAAECBAjUrIAAaM12rYYRIECAAAECBAgQIECAAAECBAgQICAA6hkgQIAAAQIECBAgQIAAAQIECBAgQKBmBQRAa7ZrNYwAAQIECBAgQIAAAQIECBAgQIAAAQFQzwABAgQIECBAgAABAgQIECBAgAABAjUrIABas12rYQQIECBAgAABAgQIECBAgAABAgQICIB6BggQIECAAAECBAgQIECAAAECBAgQqFkBAdCa7VoNI0CAAAECBAgQIECAAAECBAgQIEBAANQzQIAAAQIECBAgQIAAAQIECBAgQIBAzQoIgNZs12oYAQIECBAgQIAAAQIECBAgQIAAAQICoJ4BAgQIECBAgAABAgQIECBAgAABAgRqVkAAtGa7VsMIECBAgAABAgQIECBAgAABAgQIEBAA9QwQIECAAAECBAgQIECAAAECBAgQIFCzAgKgNdu1GkaAAAECBAgQIECAAAECBAgQIECAgACoZ4AAAQIECBAgQIAAAQIECBAgQIAAgZoVEACt2a7VMAIECBAgQIAAAQIECBAgQIAAAQIEBEA9AwQIECBAgAABAgQIECBAgAABAgQI1KyAAGjNdq2GESBAgAABAgQIECBAgAABAgQIECAgAOoZIECAAAECBAgQIECAAAECBAgQIECgZgUEQGu2azWMAAECBAgQIECAAAECBAgQIECAAAEBUM8AAQIECBAgQIAAAQIECBAgQIAAAQI1KyAAWrNdq2EECBAgQIAAAQIECBAgQIAAAQIECAiAegYIECBAgAABAgQIECBAgAABAgQIEKhZAQHQmu1aDSNAgAABAgQIECBAgAABAgQIECBAQADUM0CAAAECBAgQIECAAAECBAgQIECAQM0KdKrZltVYw8aPHx/mzJnTbKs23HDDsNFGGzV7vq0nxo4dG+bNm5e3mH79+oVNN90077laO7h48eIwZsyYRs3q2rVr2GGHHRodS9LO7Nmzw1tvvdWoSj179gxbb711o2NJ2Ln11lvDkiVLUlXZaaedwrbbbpuEaqkDAQIECBAgQIAAAQIECBAgUMUCAqBV0nk/+clPwuOPP95sbffZZ5/wxBNPNHu+LSeWLl0adt1112YDoMcff3y45ZZb2nKLqrl28uTJKYvsCm+88cZh0qRJ2YcStf3ss8+GQw89tFGd9txzz/D00083OlbpnRtvvDGcfPLJmWpcccUVAqAZDRsECBAgQIAAAQIECBAgQIBAsQKmwBcrl7DrYjBr1qxZZanVY4891mzwsyw3VGjdCXz00UfhrLPOqrt2azABAgQIECBAgAABAgQIECBQfgEB0PIbt8sdVqxYEe6///6y3Ouuu+4qS7kKJRAF4pT3Y445RpDd40CAAAECBAgQIECAAAECBAiURUAAtCyslSn0nnvuKfmNFy5cGB555JGSl6tAAlFg2bJl4bDDDgsjRowAQoAAAQIECBAgQIAAAQIECBAoi4AAaFlYK1PoM888E2bOnFnSmz/88MNh0aJFJS1TYQSiwPLly8ORRx4Z4isWJAIECBAgQIAAAQIECBAgQIBAuQQEQMslW4FyyzEN3vT3CnRkHdwyBur333//8NBDD9VBazWRAAECBAgQIECAAAECBAgQqKSAAGgl9dt478GDBzcp4d57721yrNgDn332Wfjb3/7W6PIePXo02rdDoFCBUaNGhe222y488cQThV4qPwECBAgQIECAAAECBAgQIECgYAEB0ILJknPBnnvuGfr169eoQnEa/IwZMxodK3bngQceSL2jMX19t27dwpAhQ9K7PgkUJBBHKF955ZVhjz32CB9//HFB18pMgAABAgQIECBAgAABAgQIEChWQAC0WLkEXNexY8dwxBFHNKpJKafB505/P/DAA8Oaa67Z6H52CLRGIL7nc9tttw1nn3126t2fudd0794995B9AgQIECBAgAABAgQIECBAgEBJBARAS8JYuUK+/e1vN7l5KabBx3c0Pvnkk43K/s53vtNo3w6BlgT+8Y9/pEYNx/d9jhs3rkn2hoaGcOmll4ajjjqqyTkHCBAgQIAAAQIECBAgQIAAAQKlEOhUikKUUTmBXXbZJWywwQaNphSPGDEiNQ2+b9++RVfsvvvuC3E0aTr17NkztWjN/fffnz5U0s+JEyeGV155JbWK/axZs0KnTp1CrH/82XHHHcOGG25Y0vtlF/bhhx+G9957L7z77rthypQpYZ111gkDBgwIO+ywQ9hkk02ys5ZtO04Jf+mll1L9Nn369JR9fL1B/PnKV74SBg4cWLZ7l7Pgs846K/z973/Pe4v11lsv3HTTTeHggw8OJ598ct48DhIgQIAAAQIECBAgQIAAAQIE2iogANpWwQpf36FDhzB06NBw9dVXZ2qSngb/b//2b5ljhW7kTn8/9NBDQ5cuXQotZrX5J0+eHK666qoQg6otvRPyX//1X8O3vvWt8OMf/zj07t17teW25uTKlStDfMfpFVdckQo85rsmjk6M76uM07bjCMZSp7lz54ZrrrkmVY/XX399tcXH9sfXHcT2r7322qvNWw0njz766NQz26dPn2qorjoSIECAAAECBAgQIECAAAECVSxgCnwVd1666vmmwd9zzz3p0wV/xmDkyJEjG11Xyunv8+fPDyeddFLYdNNNw+9+97sWg5+xImPHjg2XXHJJGDRoULjsssvC559/3qh+hew8++yzYfPNN08FFOOoy+bSqlWrQlxUKr779De/+U1z2Qo+HoOvMfD5hS98IfziF78ILQU/4w1i+2PeaHbDDTeEWLdqTPE9oMOHDw+33357EPysxh5UZwIECBAgQIAAAQIECBAgUH0CAqDV12dNarzTTjuFjTbaqNHxGOSLU6mLSTF4mh1gi9PQ995772KKanLNyy+/nFoM549//GOjKfZNMjZzII6aPP/888N+++0X5syZ00yu5g/HUZ/77rtvarp787kan4kW55xzTjjllFPyLuDTOPfq9xYtWhQOO+yw1EjOTz/9dPWZ85yN72b94Q9/GI499tiwbNmyPDmSeSgGrm+99dYwZsyY8I1vfCOZlVQrAgQIECBAgAABAgQIECBAoCYFBEBroFvjVO0jjzyyUUvS0+AbHWzlTu709zjFPq4439aUXhBn0qRJzRYV37+5zz77hPhu09WtDP7UU0+Fr371q2HatGnNlpV74uabb069LmDJkiW5p1L7cYr/1772tfD1r3899OjRo0meP/zhD+GYY45pcry1B+bNm5cq+6GHHmr2khhsju2Po07j+z+bS3fccUf45je/mfggaOzHBx98MMR3vMagbXxlg0SAAAECBAgQIECAAAECBAgQaE8B0Yj21C7jvUo1Df79999v8k7MYcOGtbnmcaGh+B7NOIIzN/Xq1Sv853/+ZypINnv27PDEE0+E5557LsSA4RtvvBF+/vOf5w3ATpgwIcS6ZS/WlFt2ej+269RTT82bNwYSR48enbrfqFGjQgyuxnrG0YpxtGV2ikHcYtP3v//9JraxrBh4/fd///cwderU1Kjd2P5HHnkkFdz94IMPwp/+9Ke87z2N9Tz33HOLrU67XBen7R9yyCECn+2i7SYECBAgQIAAAQIECBAgQIBAPgEB0HwqVXgsrlge3ymZnYqZBn/33XdnF5FafTyO4mtrOvHEE/O+63Pw4MGpQGNcBTy+3zI7xVGn22yzTbjoootSQcn+/ftnn05txxXvL7jggibHcw+ceeaZYenSpbmHU1PbH3300dRo0s6dO2fOx3tvt9124brrrgu33HJLmxeA+q//+q+Q772scRTra6+9Fn7605+G9ddfP3P/9EZc/f24444Lr776aqo+6ePpz7j41V/+8pf0rk8CBAgQIECAAAECBAgQIECAAIEcAQHQHJBq3s2dBh8X2/nzn/9cUJNyp7/HkaVxin1bUgzExlGNuSmuLP/888+HTTbZJPdUk/3dd989tVjQl770pSbnfvvb36ZGTzY58X8HnnzyydQ07NzzV155Zfj1r3/d4ujE448/Pjz99NOha9euuUW0aj++9/NnP/tZk7xxIaY4inPLLbdsci73wMYbb5yyyrcafQyeSgQIECBAgAABAgQIECBAgAABAvkFBEDzu1Tl0XzT4O+9995Wt2X8+PEhd4p3KVZ/v/DCC5vUIU77jiuhZ4+6bJIp50B8P2YMWuamOLLzqquuyj2c2b/22msz2+mNDTfcMJx++unp3RY/47tBWzPSNF9B//3f/x3i1P7sFEeYxqnt3bp1yz682u2Y9/rrr29i9uabb4bHH398tdc6SYAAAQIECBAgQIAAAQIECBCoVwEB0Brq+W233TbEUYXZKY6+bO1CQbmjP2NZcRp4W9Inn3wSYh1yU1xRfcCAAbmHW9yP7+vcY489muSLU8yXL1/e5HgMjsYRoLnp7LPPbhJIzM2Tux+vWXfddXMPt7gfA7256Ywzzgg77bRT7uEW9+NI0GiXm37/+9/nHrJPgAABAgQIECBAgAABAgQIECDwTwEB0Bp7DHJHgcZp8Pfff3+rWpn7/s9SjP6MU7xz05prrhnOO++83MOt3r/88sub5I0LJsX3ZOamGHxduHBho8PrrbdeOOmkkxoda81OHK2a69vSdXERo7hYU2464ogjcg+1ej/3VQfxwvgu1FWrVrW6DBkJECBAgAABAgQIECBAgAABAvUiIABaYz2dL0CXb/Gd3GbHFc9zA3WlCIDmG325zz77hBiELDbFhYPiSMjclG+k6V//+tfcbGG33XYL3bt3b3K8NQcOPvjg1mTL5Imr2eemOP1/++23zz3c6v040rdDh8a/unPmzAnxFQYSAQIECBAgQIAAAQIECBAgQIBAY4HGUZTG5+xVoUBcJGjrrbduVPORI0e2OA0+d/p7DNB98YtfbFROMTsxsJqb8gUvc/O0tJ9v4aRXXnmlyWVvv/12k2Px/Z/Fphh8LCSNGjWqSfZoG4OgxaYePXo0edVBLOvFF18stkjXESBAgAABAgQIECBAgAABAgRqVqBTzbasjhsWR4HGhXHSKb0a/A9/+MP0oUafcep07vT3YcOGNcpT7M6sWbOaXFqqAGhcmT075bvX9OnTs7OkttsSAF1//fVT7wH99NNPm5Sb78CHH37Y5HAMyu6www5Njhdy4OOPP26SPV9bm2RygAABAgQIECBAgAABAgQIECBQZwICoDXY4fEdkbkrr8dp8M0FQEePHh2mTJmSkWhoaAilCoDmCxSWKgCaqfD/beS714wZM3KzFbX4UnYhffr0CfnulZ0nvZ27+ns8Hqerx59Sp3KUWeo6Ko8AAQIECBAgQIAAAQIECBAg0N4CpsC3t3g73C9OXc9dvT2+i3Lq1Kl57547/X2XXXZpc5Aw3iguPhRXYc9N5QqAfvbZZ7m3CjNnzmxyrC0jQGNha621VpMymzvQnkHJ9rxXc+11nAABAgQIECBAgAABAgQIECCQNAEB0KT1SInqk7sYUnoafG7xK1asCPfee2+jw6VY/CgWGMvOlzp1avvA42XLljUpOjcwGaf2x3bnprbev1u3brlFNru/ePHiZs+V+kS+YHOp76E8AgQIECBAgAABAgQIECBAgEC1CbQ9ElVtLa6T+sZp8D/96U8btTYGOk877bRGx0aMGNFogaQYHBw6dGijPMXuxIBkx44dmwRCJ0+eHLbaaqtii01d98EHHzS5vm/fvo2Oxan88dgnn3zS6Hi8Nq4kX2xqbiRtvvJ69erV5PAJJ5wQ9tprrybH23pg0003bWsRridAgAABAgQIECBAgAABAgQI1JyAAGjNden/Niiukr7jjjuGl156KdPC9DT4/v37Z47lTn/fe++9w3rrrZc535aNGICMAcDc92XGAGhbU74AaL56x0WL8gVAi71/HFWab2Gj5spbZ511mpzq2bNnOOaYY5ocd4AAAQIECBAgQIAAAQIECBAgQKD0AqbAl940MSW2NA1++fLl4f77729U31JNf08Xmi8oWYoAaL4yckeAxjrEAGhuyhc8zc3T3H5cab2QqeZxwaTc9Nprr+Uesk+AAAECBAgQIECAAAECBAgQIFAmAQHQMsEmodg4lT2OwsxO9913X2b3ySefbDQ6s0uXLuHQQw/NnC/Fxg477NCkmHzByyaZWjiQL4jZr1+/Jld94QtfaHIs37VNMjVzoNBrv/zlLzcp6fXXXw9xJKlEgAABAgQIECBAgAABAgQIECBQfgEB0PIbV+wOAwYMCDvvvHOj+48cOTLMmDEjdezuu+9udG7//fcPa6+9dqNjbd2JK8rnpvje0QULFuQebvX+G2+8EfIFUffcc88mZRx88MFNjo0ePTrMnz+/yfHWHBgzZkxrsmXy7Lbbbpnt9MbcuXPDhAkT0rtFfZ5//vnhiiuuCLEPX3zxxRBHpgqqFkXpIgIECBAgQIAAAQIECBAgQKDGBQRAa7yD42JI2Smuiv7AAw+Ezz//PDz44IPZp0Kpp7/HwnffffdG94g7M2fODFdffXWT4609cOGFFzYJ9m2wwQZNgr2xvBgUXXfddRsVPXv27HDNNdc0OtaanWgWg46FpK985Suha9euTS75+c9/3uRYaw888sgj4bLLLgvnnntuGDZsWGpBpzjVP47olQgQIECAAAECBAgQIECAAAECBBoLCIA29qi5vTgNvkOHxt0cA6BPP/10mDNnTqa9ccX2Aw88MLNfqo242nu+FdevvPLKEAORhaY42jEGAHPT4Ycf3mS6f8wTV7U/5JBDcrOHq666KsSRmIWkW265Je/I09WVEV8rkG/Bo3vuuSc8/vjjq7u02XOXXHJJk3Nf+tKXwj777NPkuAMECBAgQIAAAQIECBAgQIAAgXoXaBwZq3eNGmx/XPE9dxr2U089Ff74xz82am0MEnbr1q3RsVLtnHnmmU2KisHHQkdBxsWHzjnnnCZlxQNxJGRz6ZRTTmkSBI7B31/96lfNXdLk+KJFi8Kll17a5HhrDsT2576LNV532mmnhSVLlrSmiEye3/72t+Hll1/O7Kc3fvSjH6U3fRIgQIAAAQIECBAgQIAAAQIECGQJCIBmYdTqZu5q8HEqdxyBmJ3KMf09Xf5hhx0Wtt122/Ru5vP6668PRxxxRJg3b17mWHMbkyZNCvF9os8++2yTLCeffHL42te+1uR4+sDgwYNTwcb0fvrzN7/5TYjB0eixuhTvHd+l+uGHH64uW7Pntthii3DUUUc1OT9x4sTU6Ng333yzybl8B/70pz+FfMHk3r17h2OPPTbfJY4RIECAAAECBAgQIECAAAECBOpeQAC0Dh6BOD28Y8eOzbY0viNzyJAhzZ5v64k4Bf/ee+8NcZp9bvrzn/8c4krxMbC5fPny3NNh8eLFqYV+tt9++/Dqq682Ob/llluGOCqypRRHb2644YZNsv3hD38Ie+21V95FiWJ9HnrooRADqHHhpbakG264IWy66aZNiojlxvLjlPyFCxc2OR8PxEDpcccdF773ve81efdptI2B5O7du+e91kECBAgQIECAAAECBAgQIECAQL0LdKp3gHpof9++fcPXv/718Pe//z1vc+MozDXWWCPvuVId3GyzzUJ8h2YcjbpixYpGxb777rthjz32SC0W9OUvfzkVEIxTw+NU7zg6Mjd/+uJ11lkn3HXXXa0K/sXg62233RYOOuigJivQP/fcc2HzzTcPcSGhOMp0o402SgVb4/3j1PfstPHGG6fqU+ho0LXXXjsVBI6LMuW+ezRO7Y8jO88+++zwxS9+MUSDeJ8pU6akgp9x5fnmDH73u9+tdvp/dt1tEyBAgAABAgQIECBAgAABAgTqUUAAtE56PQYemwuAlnP6ezZvHIn617/+NcSV6XODgDFfDHq+8MILqZ/s6/Jtx9XV4zT+GChsbYrBxxEjRoT9998/TJ8+vcll06ZNC3FEanOpR48e4cEHH0wtalRoADSWGQObo0aNSi02FafV56aVK1eGt99+O/WTey7fflwM6dRTT813yjECBAgQIECAAAECBAgQIECAAIH/EzAFvk4ehUMPPTTvKM8NNtigySJJ5ST5xje+EUaPHh1iMLKYFKfyxwV/4qjNQoKf6XvFqfTx/tttt136UKs+4/T5xx57LO+7TFtVwP9liqu1x5Xs4ztB4/T1YtJWW22VCv5ecMEFxVzuGgIECBAgQIAAAQIECBAgQIBAXQkUF4GpK6LaaGx8z+c+++zTpDFxNGaxgbgmhbXyQHxv59NPPx2GDx+eqlPnzp1bvHLgwIHhoosuCpMnTw7XXHNNaM01zRW6ySabhDit/C9/+UvYddddm8uWOr7mmmuGk046KfUO0N122221eVt7cr311gt33HFHqsw4+rZXr16tunTrrbdOBT7Hjh0bhg4d2qprZCJAgAABAgQIECBAgAABAgQI1LtAw6p/pnpH0P7KCsTFf5566qkwbty4MGPGjDBz5szQrVu31Ls44/s4v/CFL4SddtqpbIHa+J7ReO+42NB7772XGikbR3zG94IeeOCBIU59L2eK7/eM7xuN0//T7Y+LP8W2Dxo0KNX++DlgwIDQ0NBQzqoomwABAgQIECBAgAABAgQIECBQcwICoDXXpRpEgAABAgQIECBAgAABAgQIECBAgEBawBT4tIRPAgQIECBAgAABAgQIECBAgAABAgRqTkAAtOa6VIMIECBAgAABAgQIECBAgAABAgQIEEgLCICmJXwSIECAAAECBAgQIECAAAECBAgQIFBzAgKgNdelGkSAAAECBAgQIECAAAECBAgQIECAQFpAADQt4ZMAAQIECBAgQIAAAQIECBAgQIAAgZoTEACtuS7VIAIECBAgQIAAAQIECBAgQIAAAQIE0gICoGkJnwQIECBAgAABAgQIECBAgAABAgQI1JyAAGjNdakGESBAgAABAgQIECBAgAABAgQIECCQFhAATUv4JECAAAECBAgQIECAAAECBAgQIECg5gQEQGuuSzWIAAECBAgQIECAAAECBAgQIECAAIG0gABoWsInAQIECBAgQIAAAQIECBAgQIAAAQI1JyAAWnNdqkEECBAgQIAAAQIECBAgQIAAAQIECKQFBEDTEj4JECBAgAABAgQIECBAgAABAgQIEKg5AQHQmutSDSJAgAABAgQIECBAgAABAgQIECBAIC3QKb3hkwABAgQIECBAgACBygqsXLkyLF26tLKVWM3dO3fuHDp27LiaHE4RIECAAAECBJInIACavD5RIwIECBAgQIAAgToVGDFiRNhrr70S2/q77747HHnkkYmtn4oRIECAAAECBPIJmAKfT8UxAgQIECBAgAABAgQIECBAgAABAgRqQsAI0JroRo0gQIAAAQIECBCoNYE+ffqEAQMGVLxZU6dODdOmTat4PVSAAAECBAgQIFCsgABosXKuI0CAAAECBAgQIFBGgaFDh4YbbrihjHdoXdG/+MUvwsUXX9y6zHIRIECAAAECBBIoYAp8AjtFlQgQIECAAAECBAgQIECAAAECBAgQKI2AAGhpHJVCgAABAgQIECBAgAABAgQIECBAgEACBQRAE9gpqkSAAAECBAgQIECAAAECBAgQIECAQGkEBEBL46gUAgQIECBAgAABAgQIECBAgAABAgQSKCAAmsBOUSUCBAgQIECAAAECBAgQIECAAAECBEojIABaGkelECBAgAABAgQIECBAgAABAgQIECCQQAEB0AR2iioRIECAAAECBAgQIECAAAECBAgQIFAaAQHQ0jgqhQABAgQIECBAgAABAgQIECBAgACBBAoIgCawU1SJAAECBAgQIECAAAECBAgQIECAAIHSCAiAlsZRKQQIECBAgAABAgQIECBAgAABAgQIJFBAADSBnaJKBAgQIECAAAECBAgQIECAAAECBAiURkAAtDSOSiFAgAABAgQIECBAgAABAgQIECBAIIECAqAJ7BRVIkCAAAECBAgQIECAAAECBAgQIECgNAICoKVxVAoBAgQIECBAgAABAgQIECBAgAABAgkUEABNYKeoEgECBAgQIECAAAECBAgQIECAAAECpREQAC2No1IIECBAgAABAgQIECBAgAABAgQIEEiggABoAjtFlQgQIECAAAECBAgQIECAAAECBAgQKI2AAGhpHJVCgAABAgQIECBAgAABAgQIECBAgEACBQRAE9gpqkSAAAECBAgQIECAAAECBAgQIECAQGkEBEBL46gUAgQIECBAgAABAgQIECBAgAABAgQSKCAAmsBOUSUCBAgQIECAAAECBAgQIECAAAECBEojIABaGkelECBAgAABAgQIECBAgAABAgQIECCQQAEB0AR2iioRIECAAAECBAgQIECAAAECBAgQIFAaAQHQ0jgqhQABAgQIECBAgAABAgQIECBAgACBBAoIgCawU1SJAAECBAgQIECAAAECBAgQIECAAIHSCAiAlsZRKQQIECBAgAABAgQIECBAgAABAgQIJFBAADSBnaJKBAgQIECAAAECBAgQIECAAAECBAiURqBTaYpRCgECBAgQIECAAAECBNpHYN68eeHzzz9vn5sVeJeOHTuGXr16FXiV7AQIECBAgEA5BQRAy6mrbAIECBAgQIAAAQIESi5w4IEHhpEjR5a83FIUuOWWW4bx48eXoihlECBAgAABAiUSMAW+RJCKIUCAAAECBAgQIECAAAECBAgQIEAgeQJGgCavT9SIAAECBAgQIFCTAitXrkx0uzp0MDYg0R3UTOW+8pWvhM6dOzdztn0Or1q1KowaNap9buYuBAgQIECAQMECAqAFk7mAAAECBAgQINBYYOLEieF3v/td44MJ2jv++OPD9ttvX/Eaff3rXw/PPvtsxeuRrwJbbLFFeOutt/KdcizhAo8++mjo27dvRWu5YsWK0KnT//7Tatq0aeFHP/pRReuTe/PddtstDB06NPewfQIECBAgUDcCAqB109UaSoAAAQIECJRL4OOPP050AHSXXXZJRAC0XP7KJVBpgTgCNJ3mzJmTuD8PYoBWADTdQz4JECBAoB4FBEDrsde1mQABAgQIECBQYYEvfvGLmRFzlayKxWoqqe/eBAgQIECAAIH2ERAAbR9ndyFAgAABAgTqROCAAw4IZ5xxRsVbe+utt4bbb7+94vVorgJxKny/fv2aO90ux+OoPe/9bBfqurrJwIEDw0033VTxNr/22mvh3HPPrXg9VIAAAQIECCRBQAA0Cb2gDgQIECBAgEDNCAwYMCAMGTKk4u15/vnnK14HFSBQjwI9evRIxJ8BHTt2rEd+bSZAgAABAnkFLHWZl8VBAgQIECBAgAABAgQIECBAgAABAgRqQUAAtBZ6URsIECBAgAABAgQIECBAgAABAgQIEMgrIACal8VBAgQIECBAgAABAgQIECBAgAABAgRqQcA7QGuhF7WBAAECBAgQIECgYIG4CFI6ffTRR+GQQw5J71bsc9asWRW7txsTIECAAAECBGpVQAC0VntWuwgQIECAAAECBFotsGDBgvDwww+3Or+MBAgQIECAAAEC1SNgCnz19JWaEiBAgAABAgQIECBAgAABAgQIECBQoIARoAWCyU6AAAECBAgQIFB7AoMGDQp//etfK96wW265Jfz617+ueD1UgAABAgQIECBQSwICoLXUm9pCgAABAgQIECBQlEDnzp3D5ptvXtS1pbyob9++pSxOWQQIECBAgAABAv8UMAXeY0CAAAECBAgQIECAAAECBAgQIECAQM0KCIDWbNdqGAECBAgQIECAAAECBAgQIECAAAECAqCeAQIECBAgQIAAAQIECBAgQIAAAQIEalbAO0Brtms1jAABAgQIEKhngVdeeSXT/HPPPTdcfvnlmf1KbUycOLFSt3ZfAgQIECBAgACBOhYQAK3jztd0AgQIECBAoHYF5s+fn2ncBx98EOKPRIAAAQIECBAgQKAeBUyBr8de12YCBAgQIECg5gUaGhpqvo0aSIAAAQIECBAgQKA1AkaAtkZJHgIECBAgQIBAlQmsWrUqU+Nbb701DB06NLNfqY1+/fqFefPmVer27kuAAAECBAgQIFCnAgKgddrxmk2AAAECBAjUtkD2CNDOnTuHrl27VrzB2XWqeGVUgAABAgQIECBAoG4ETIGvm67WUAIECBAgQIAAAQIECBAgQIAAAQL1JyAAWn99rsUECBAgQIAAAQIECBAgQIAAAQIE6kZAALRuulpDCRAgQIAAAQIECBAgQIAAAQIECNSfgABo/fW5FhMgQIAAAQIECBAgQIAAAQIECBCoGwGLINVNV2soAQIECBAgQIAAgcIF3n777cxFv//978Pw4cMz+5XamDBhQqVu7b4ECBAgQIBAFQoIgFZhp6kyAQIECBAgQIAAgfYSmDZtWuZWzzzzTIg/UvIF5s6dm6nkpEmTwiOPPJLZT8LG4MGDQ//+/ZNQFXUgQIAAgToQEACtg07WRAIECBAgQIAAAQIE6kvg/fffzzT4scceC/EnSemee+4JQ4cOTVKV1IUAAQIEalhAALSGO1fTCBAgQIAAAQIECJRS4OKLLw577713KYssqqxvfOMbYeHChUVd6yICBAgQIECg/gQEQOuvz7WYAAECBAgQIECAQFECm2++edh5552LuraUF3Xs2LGUxdV8WV/+8pfDkCFDKt7O5557LowePbri9VABAgQIEKg/AQHQ+utzLSZAgAABAgQIECBAoI4EYtD6iiuuqHiLf/7znwuAVrwXVIAAAQL1KdChPput1QQIECBAgAABAgQIECBAgAABAgQI1IOAAGg99LI2EiBAgAABAgQIECBAgAABAgQIEKhTAVPg67TjNZsAAQIECFSzwKOPPpqo6o8dOzZR9VEZAgQIECBAgAABAgT+v4AA6P+3sEWAAAECBAhUicBBBx1UJTVVTQIECBAgQIAAAQIEKi1gCnyle8D9CRAgQIAAAQIECBAgQIAAAQIECBAom4ARoGWjVTABAgQIECBQboHevXuHE044ody3abH8V199NTzzzDMt5pOBAAECBAgQIECAAIH2FxAAbX9zdyRAgAABAgRKJNC3b99w5ZVXlqi04ou5+uqrBUCL53MlAQIECBAgQIAAgbIKmAJfVl6FEyBAgAABAgQIECBAgAABAgQIECBQSQEB0ErquzcBAgQIECBAgAABAgQIECBAgAABAmUVEAAtK6/CCRAgQIAAAQIECBAgQIAAAQIECBCopIAAaCX13ZsAAQIECBAgQIAAAQIECBAgQIAAgbIKCICWlVfhBAgQIECAAAECBAgQIECAAAECBAhUUsAq8JXUd28CBAgQIFAFAr/85S/D+PHjq6CmqkiAAAECBAgQIECAAIGmAgKgTU0cIUCAAAECBLIEnnnmmTBixIisIzYJECBAgAABAgQIECBQPQKmwFdPX6kpAQIECBAgQIAAAQIECBAgQIAAAQIFChgBWiCY7AQIECBAoJ4FnnrqqdCnT5+KEqxatSpsu+22Fa2DmxMgQIAAAQIECBAgUD0CAqDV01dqSoAAAQIEKi6w5ZZbhvXXX7/i9VABAgQIECBAgAABAgQItFbAFPjWSslHgAABAgQIECBAgAABAgQIECBAgEDVCQiAVl2XqTABAgQIECBAgAABAgQIECBAgAABAq0VEABtrZR8BAgQIECAAAECBAgQIECAAAECBAhUnYAAaNV1mQoTIECAAAECBAgQIECAAAECBAgQINBaAYsgtVZKPgIECBAg0A4CV155ZYg/SUqzZ89OUnXUhQABAgQIECBAgAABAgUJCIAWxCUzAQIECBAor8CCBQvC9OnTy3sTpRMgQIAAAQIECBAgQKCOBARA66izNZUAAQIEqkuge/fuoXPnzhWv9GeffVbxOqgAAQIECBAgQIAAAQIEihUQAC1WznUECBAgQKAMAg0NDZlS//jHP4Zhw4Zl9iu10atXrzB37txK3d59CRAgQIAAAQIECBAg0CYBAdA28bmYAAECBAgQIECAAAECBFojsGzZsky2OLsgaa986du3b8j+H5GZytogQIAAgaoXEACt+i7UAAIECBAgQIAAAQIECCRfYNSoUZlKnnzyySH+JCnNmDEjrLfeekmqkroQIECAQIkEOpSoHMUQIECAAAECBAgQIECAAAECBAgQIEAgcQJGgCauS1SIAAECtSuwdOnSxDauY8eOoVMnfy0mtoNUjAABAgRqSuBLX/pSIkZbvv76695zXVNPlsYQIEAgv4B/6eV3cZQAAQIEyiDQtWvXMpRamiJ/8IMfhD/84Q+lKUwpBAgQIECAwGoFfvnLX4ahQ4euNk97nNxtt93Cc8891x63cg8CBAgQqKCAKfAVxHdrAgQIECBAgAABAgQIECBAgAABAgTKK2AEaHl9lU6AAAECeQS6dOkSNt988zxn2vfQwoULw3vvvde+N3U3AgQIECBAgAABAgQIEGhXAQHQduV2MwIECBCIAhtvvHF44403Ko4xYsSIsOeee1a8HipAgAABAgQIVEZg2rRpmRv/6le/Ct27d8/sV3qjX79+4Ywzzqh0NdyfAAECNSEgAFoT3agRBAgQIECAAAECBAgQIFCowIwZMzKXXHPNNZntJGzEhaIEQJPQE+pAgEAtCHgHaC30ojYQIECAAAECBAgQIECAAAECBAgQIJBXwAjQvCwOEiBAgAABAgQIECBAgEA9Cdxyyy2hZ8+eFW3yihUrwtChQytaBzcnQIBALQoIgNZir2oTAQIECBAgQIAAAQIECBQkcMABB4T11luvoGtKnTkGQCUCBAgQKL2AKfClN1UiAQIECBAgQIAAAQIECBAgQIAAAQIJERAATUhHqAYBAgQIECBAgAABAgQIECBAgAABAqUXMAW+9KZKJECgDgWeeuqp8OSTTya25T/96U/DWmutldj6VapiU6dOzdz6pZdeCueff35mv1IbI0eOrNSt3ZcAAQIECBAgQIAAAQI1KSAAWpPdqlEESitwzDHHhFWrVpW20BKVttdee4UTTzyxRKUVX0wMWl122WXFF1DmK0877TQB0DzG06dPzxx97bXXQvyRCBAgQIAAAQIECBAgQKC2BARAa6s/tYZAWQTuvPPOxAZA11xzzUQEQBsaGspir1ACBAgQIECAAAECBAgQIECgbQICoG3zczUBAgRSAtkjZH/84x+HXXfdteIyv/jFL8K4ceMqXo9qqcCQIUPCD37wg4pXV79VvAtUgAABAgQIVEwg+zvlrFmzwpVXXlmxuuS78XbbbRf23nvvfKccI0CAQKIFBEAT3T0qRyBZAgMHDgz3339/xSs1ZsyYcPLJJ1e8HtkVyB4BuuOOO4bDDz88+3RFtq+77rqK3Ldabzpo0CD9Vq2dp94ECBAgQKBGBFauXJlpyYwZM8LZZ5+d2U/Cxg9/+EMB0CR0hDoQIFCwgABowWQuIFC/Al27dg077LBDxQEWLlxY8TqoAAECBAgQIECAAAECBAgQIFAdAgKg1dFPakmAAAECBAgQIECAAAECdSTwL//yL+Hiiy+ueIvHjx8frrrqqorXQwUIECDQFgEB0LbouZYAAQIECBAgQIAAAQIECJRBoFevXolY7PPJJ58UAC1D/yqSAIH2FejQvrdzNwIECBAgQIAAAQIECBAgQIAAAQIECLSfgABo+1m7EwECBAgQIECAAAECBAgQIECAAAEC7SwgANrO4G5HgAABAgQIECBAgAABAgQIECBAgED7CQiAtp+1OxEgQIAAAQIECBAgQIAAAQIECBAg0M4CFkFqZ/B6vt24cePCz372s8QSnHnmmWGPPfZIbP1UjAABAgQIECBAgAABAgQIECBAoHABAdDCzVxRpMDs2bPDI488UuTV5b9s2LBh5b+JOxAgQIAAAQIECBAgQIAAAQIECLSrgCnw7crtZgQIECBAgAABAgQIECBAgAABAgQItKeAEaDtqe1eGYEjjzwyXHrppZn9Sm1cd9114dprr63U7d2XAAECBAgQIECAAAECBAgQIECgzAICoGUGVnx+gZ49e4bNNtss/8l2PNqnT592vJtbESBAgAABAgQIECBAgAABAgQItLeAKfDtLe5+BAgQIECAAAECBAgQIECAAAECBAi0m4ARoO1G7UYECBAgQIAAAQIECBAgQKC6BJYtW5ap8Lx588KkSZMy+0nY6Nu3b+jRo0cSqqIOBAgkWEAANMGdo2oECBAgQIAAAQIECBAgQKCSAm+++Wbm9rfddluIP0lK9957bzjiiCOSVCV1IUAggQICoAnsFFUiQIAAAQIECBAgQIAAAQIEWhY488wzwyWXXNJyxnbMMWLEiNCrV692vKNbESDQkoAAaEtCzhMgQIAAAQIECBAgQIAAAQJh4MCBYauttqq4xAsvvBDmzJmTqseUKVNC/ElSWr58eZKqoy4ECPxTQADUY0CAAAECBAgQIECAAAECBAi0KHDQQQeF6667rsV85c6w2267heeee67ct1E+AQI1JCAAWkOdqSkECBAgQIAAAQIECBAgQKCeBO68885w2GGHVbzJe+21Vxg1alTF66ECBAjkFxAAze/iKAECBAgQIECAAAECBAgQIJBwgTXWWCN06dKl4rVsaGioeB1UgACB5gU6NH/KGQIECBAgQIAAAQIECBAgQIAAAQIECFS3gBGg1d1/ak+AAAECBAgQIECAAAECBAgQaCSwYsWKcPjhhzc6lqSdAw44IHz/+99PUpXUpcYFBEBrvIM1jwABAgQIECBAgAABAgQIECivQHpV+niXu+66K6y11lrlvWELpccA6EMPPdRCrsqdHjBgQOVu7s51KSAAWpfdrtEECBAgQIAAAQIECBAgQIBAqQQ+/PDDTFGnn356ZtsGAQLJEBAATUY/qAUBAgQIECBAgAABAgQIECBAoOQCm222WXj44YdLXm6hBY4aNSqceOKJhV4mP4GSCAiAloRRIQQIECBAgAABAgQIECBAgACBEC644ILQo0ePilJ8/vnn4ec//3mqDp07dw5bbLFFResTb/7RRx9VvA65FXjrrbfChAkTcg8nZn///fcPa6yxRmLqU80VaVj1z1TNDVD36hEYOXJk2H333VMVXnPNNUOfPn0qXvnPPvssxJ+YYn1ivZKQttpqqzBu3LgkVCVVh8mTJ6c+O3XqFDbccMOK12vJkiVh2rRpqXok8Vlab731Kv6FJ+JEo2gVU+y32H+VTkl7lubNmxdmz56dYonvaVp33XUrTRSmTp0ali5dmqpHUp6lDz74IKS/LsT3NXXs2LHiTulnKX4h3GCDDSpen7lz54b0u788S813R9KepfhcxzrF5Flqvt/8udS8TfqMZyktsfrPpD9LAwcODB06dFh9I8p81rPUOmDPUstOSXyWFi9eHKZPn56qfFK+L8Xvb/F7XFLTrFmzEvFvlKT6FFIvAdBCtORtk0B2ALRNBdXBxTvuuGN46aWX6qClmkiAAAECBAgQIECAAAECBAjkExAAzadS3LHKDwcqrt6uqkKBhoaGEIfeJy3F/zMWpwfEEU1JGNUUfeIolCRZLVu2LFE+6Wco9lt8rpIwsjGushh/ktRv0WnlypVh+fLlqWcqWlU6Je1ZSvtElzjiI0nPUqxTUv5cir9r8c/KmOKfT0l5lmJ9Yl1inSqdkv4sxWe70qOaYh8l7VlKfweIdfMsRYX8Kf13XDzrWcpv5FnK75J71LOUK9J037PU1CTfkfj9Nv7dG5M/l/IJhdR3t/j3bkxJ+TsuXdMk/Tsu/Swl7d9xaaskfO9O16XaP40ArfYeVH8CBAgQIECAAAECBAgQIECAAAECBJoVqOwLTpqtlhMECBAgQIAAAQIECBAgQIAAAQIECBBou4AAaNsNlUCAAAECBAgQIECAAAECBAgQIECAQEIFBEAT2jGqRYAAAQIECBAgQIAAAQIECBAgQIBA2wUEQNtuqAQCBAgQIECAAAECBAgQIECAAAECBBIqIACa0I5RLQIECBAgQIAAAQIECBAgQIAAAQIE2i4gANp2QyUQIECAAAECBAgQIECAAAECBAgQIJBQAQHQhHaMahEgQIAAAQIECBAgQIAAAQIECBAg0HYBAdC2GyqBAAECBAgQIECAAAECBAgQIECAAIGECgiAJrRjVIsAAQIECBAgQIAAAQIECBAgQIAAgbYLCIC23VAJBAgQIECAAAECBAgQIECAAAECBAgkVEAANKEdo1oECBAgQIAAAQIECBAgQIAAAQIECLRdQAC07YZKIECAAAECBAgQIECAAAECBAgQIEAgoQICoAntGNUiQIAAAQIECBAgQIAAAQIECBAgQKDtAgKgbTdUAgECBAgQIECAAAECBAgQIECAAAECCRUQAE1ox6gWAQIECBAgQIAAAQIECBAgQIAAAQJtFxAAbbuhEggQIECAAAECBAgQIECAAAECBAgQSKiAAGhCO0a1CBAgQIAAAQIECBAgQIAAAQIECBBou4AAaNsNlUCAAAECBAgQIECAAAECBAgQIECAQEIFBEAT2jGqRYAAAQIECBAgQIAAAQIECBAgQIBA2wU6tb0IJRBoncCyZcvCp59+msrcrVu30KtXr9ZdKFdGYOHChWHevHmhf//+mWM2WiewcuXKMH369FTmNdZYI/Tp06d1F8qVEVi6dGmYPXt2yi4aSoUJzJgxI6xYsSJ1kd/hwuxi7vTvcPy7I/4dIhUmEH934+9wTOutt17o1MlXwMIEQ+rvkPjsrb322oVeWvf558+fHxYsWJBy8Dtc3OMQf4dj6t27d3EF1PFVixcvDp999llKYM011wxrrbVWHWsU1/T474/o2K9fv+IKqOOrli9fHmbOnJkS6NKli9/hIp6F9O9wfP46dDCGr1DCqVOnpi7p2LFj6Nu3b6GX11R+T09NdafGECBAgAABAgQIECBAgAABAgQIECCQLSAAmq1hmwABAgQIECBAgAABAgQIECBAgACBmhIQAK2p7tQYAgQIECBAgAABAgQIECBAgAABAgSyBQRAszVsEyBAgAABAgQIECBAgAABAgQIECBQUwICoDXVnRpDgAABAgQIECBAgAABAgQIECBAgEC2gABotoZtAgQIECBAgAABAgQIECBAgAABAgRqSkAAtKa6U2MIECBAgAABAgQIECBAgAABAgQIEMgWEADN1rBNgAABAgQIECBAgAABAgQIECBAgEBNCQiA1lR3agwBAgQIECBAgAABAgQIECBAgAABAtkCAqDZGrYJECBAgAABAgQIECBAgAABAgQIEKgpAQHQmupOjSFAgAABAgQIECBAgAABAgQIECBAIFtAADRbwzYBAgQIECBAgAABAgQIECBAgAABAjUlIABaU92pMQQIECBAgAABAgQIECBAgAABAgQIZAsIgGZr2CZAgAABAgQIECBAgAABAgQIECBAoKYEBEBrqjs1hgABAgQIECBAgAABAgQIECBAgACBbAEB0GwN2wQIECBAgAABAgQIECBAgAABAgQI1JSAAGhNdafGECBAgAABAgQIECBAgAABAgQIECCQLSAAmq1hmwABAgQIECBAgAABAgQIECBAgACBmhIQAK2p7tQYAgQIECBAgAABAgQIECBAgAABAgSyBQRAszVsEyBAgAABAgQIECBAgAABAgQIECBQUwICoDXVnRpDgAABAgQIECBAgAABAgQIECBAgEC2gABotoZtAgQIECBAgAABAgQIECBAgAABAgRqSkAAtKa6U2MIECBAgAABAgQIECBAgAABAgQIEMgWEADN1rBNgAABAgQIECBAgAABAgQIECBAgEBNCQiA1lR3agwBAgQIECBAgAABAgQIECBAgAABAtkCAqDZGrYJECBAgAABAgQIECBAgAABAgQIEKgpAQHQmupOjSFAgAABAgQIECBAgAABAgQIEKhmgXnz5lVz9RNZdwHQRHaLShEgQIAAAQIECBAgQIAAAQIECNSjwLhx4+qx2WVtswBoWXkVToAAAQIECBAgQIAAAQIECBAgQKB1AlOmTAlGgLbOqpBcAqCFaMlLgAABAgQIECBAgAABAgQIECBAoEwCY8eOLVPJ9V2sAGh997/WEyBAgAABAgQIECBAgAABAgQIJEBgwYIFYfLkyQmoSe1VQQC09vpUiwgQIECAAAECBAgQIECAAAECBKpM4K233gorV66sslpXR3UFQKujn9SSAAECBAgQIECAAAECBAgQIECgRgVWrVoVLH5Uvs4VAC2frZIJECBAgAABAgQIECBAgAABAgQItCgQp77Pnz+/xXwyFCcgAFqcm6sIECBAgAABAgQIECBAgAABAgQIlETA6M+SMDZbiABoszROECBAgAABAgQIECBAgAABAgQIECivQBz5afGj8hoLgJbXV+kECBAgQIAAAQIECBAgQIAAAQIEmhWIoz/jO0Cl8gkIgJbPVskECBAgQIAAAQIECBAgQIAAAQIEmhWIq77H1d+l8goIgJbXV+kECBAgQIAAAQIECBAgQIAAAQIE8grEqe8LFizIe87B0gkIgJbOUkkECBAgQIAAAQIECBAgQIAAAQIEWi0wduzYVueVsXgBAdDi7VxJgAABAgQIECBAgAABAgQIECBAoCiBuXPnhilTphR1rYsKExAALcxLbgIECBAgQIAAAQIECBAgQIAAAQJtFnjzzTfbXIYCWicgANo6J7kIECBAgAABAgQIECBAgAABAgQIlERg+fLlYfz48SUpSyEtCwiAtmwkBwECBAgQIECAAAECBAgQIECAAIGSCUyYMCEsWbKkZOUpaPUCAqCr93GWAAECBAgQIECAAAECBAgQIECAQEkF/vGPf5S0PIWtXkAAdPU+zhIgQIAAAQIECBAgQIAAAQIECBAomcDUqVPDzJkzS1aegloWEABt2UgOAgQIECBAgAABAgQIECBAgAABAiURMPqzJIwFFSIAWhCXzAQIECBAgAABAgQIECBAgAABAgSKE1i4cGF49913i7vYVUULCIAWTedCAgQIECBAgAABAgQIECBAgAABAq0XGDduXFi5cmXrL5CzJAICoCVhVAgBAgQIECBAgAABAgQIECBAgACB5gVi4PPNN99sPoMzZRMQAC0brYIJECBAgAABAgQIECBAgAABAgQI/K9AnPoep8BL7S8gANr+5u5IgAABAgQIECBAgAABAgQIECBQZwIWP6pchwuAVs7enQkQIECAAAECBAgQIECAAAECBOpAYObMmWHq1Kl10NJkNrHqA6BLliwJY8eODfPnz6+Y8P333x8OPvjg1M9HH31UsXq4MQECBAgQIECAAAECBAgQIECAQPIEjP6sbJ8kOgA6Y8aM8PTTT+cVuu+++8J+++0XevfuHbbZZpvQs2fPcNBBB4U33ngjb/5yHZw0aVK4/vrrw5w5c1I/K1asKNetlEuAAAECBAgQIECAAAECBAgQIFBlAkuXLg0TJkyoslrXVnUTGQAdPXp0OOSQQ8IGG2wQLrjggibiTz31VBg2bFgYPnx4WLx4cer8qlWrwqOPPhp23nnn8NhjjzW5phwHPv/883DxxReHZcuWlaN4ZRIgQIAAAQIECBAgQIAAAQIECFS5QFz5ffny5VXeiuontORVAABAAElEQVSufqekVf/FF18M++yzT1i0aFGqau+9916jKn7yySfh29/+dmhupGW8Lp5/5513Qv/+/RtdW+qdG2+8McQVvCQCBAgQIECAAAECBAgQIECAAAECuQIrV65s99nKuXWwH0LiRoCefvrpmeBn7KA11lgjxJGW6XTTTTeFWbNmpXfDqaeeGmLQ9I477ggDBgxIHY/vA73ooosyecqx8dprr4W77rorVXS3bt3KcQtlEiBAgAABAgQIECBAgAABAgQIVLFAnPq+cOHCKm5BbVQ9UQHQ119/Pbz88ssp2a5du4bbb789TJkyJRUETXPfeeed6c2w5557pt6/ueOOO4ajjjoqjBo1KnTv3j11fsSIEZl8pd6IAdZLL700xGn38d5f+9rXMrdoaGjIbNsgQIAAAQIECBAgQIAAAQIECBCoX4ExY8bUb+MT1PJEBUDHjRuXobnwwgvD0UcfHbIDihMnTkxNbU9nOumkk9Kbqc8NN9wwnHDCCantckbY/+M//iPEBZrWWmut8LOf/axRHRtVyA4BAgQIECBAgAABAgQIECBAgEBdCsRBfZ9++mldtj1pjU5UAHT8+PEZn9zgZjyRvbhRhw4dwr777pvJn97YbrvtUpvlesfC448/Hp588snUPc4666zQp0+f9K19EiBAgAABAgQIECBAgAABAgQIEEgJGP2ZnAchkQHQHj16hL59+zZRyg6ADh48OG/wMXvho+yAapPCijgwbdq0cNVVV6WuHDJkSNhrr72KKMUlBAgQIECAAAECBAgQIECAAAECtSwwc+bM8OGHH9ZyE6uqbYlaBf6zzz5rFm/JkiUh+72e+UZ/xovj1PR06tWrV3qzzZ9xRGl872d8cW0Mzv7kJz9pc5mrK2Dp0qWpd4yuLk+1nctezGrFihUh9qlUmMDy5ctTF7ArzC3mju/sTaf4+8wwrdH6z/Tv8LJly0L8HZYKE8h+Bj1/hdnF3Gm/+Bxmvx6n8JLq84r45146xe8Y6b9P0sd8tiwQn8Ho5ve3ZavcHNnPm9/hXJ3W7ad/hz1/rfPKzpX+/hKP+R3Olmn9dnSLfwZ6/lpvls6Z/Z3Zv0HSKoV9pn+H4/eXavsOGBfszv47sJCWx3aX8ncuSb/DHTt2bLTWTyEubcmbqADooEGDwjPPPJMKMsaFhuI7NtMpBj8XL16c3s07/T2ezB71udFGG2Xyt3Xjf/7nf8Ibb7yRKua8885rVLe2lp3v+hgMTn/RyXe+2o/FAEr8kYoTmDNnTnEXuiolEL+IMCz+YZg3b17xF7syJeD5K/5BiP8j0iqaxfvFK/0OF+8X//EVf6TiBfwOF28Xr/T3R9v8YjChlAGFttWm+q72/LWtz2JAi2HxhqsbMFd8qeW7Mv599+abbxYd11mwYEFJn5cYX0rK8xcXL+/Zs2f58JspOVFT4DfddNNMNR9++OHMdtyIK8KnU+/evcNXv/rV9G7mM/5C3HTTTZn9gQMHZrbbsvHOO++EG2+8MVXE0KFDww477NCW4lxLgAABAgQIECBAgAABAgQIECBQowJxcF4tD2qrxm5L1AjQAw44IMTRlTHFz7ig0RZbbBHuuOOORgHQYcOGhThkNjvNmjUrHHnkkZmI9u677x769euXnaWo7fh/+S+++OLUdM+NN944/OAHPyiqnEIvihHxWvtliaPu0qMmYv916dKlUJa6zx+Hz8eRs/H5kAoTiEP+06PI4yJqXbt2LawAuVN/Dsbf4WgXDaXCBOLzF5/DmPwOF2aXzr1o0aLU3x253wHS5302LxB/d9PT8PwON++0ujPx+evUqVPo3Lnz6rI5l0cgfndJTwGMftFRKkwg/R3a9+fC3GLu9PfnuO13OCoUntK/w76/FG4X/02fHnXs38CF+8Ur0nGEanr+4u/MpEmT2jTNO/55X4o2x+8vMcXXB3Tr1i21Xen/VOq7VKK+fWyzzTapqe3Dhw8PU6ZMCVtttVVYe+21G03Vil/aTznllEx/xRfKXnDBBeHRRx8Ns2fPzhyPx0qRrr/++lRd4h9WF154YbsF7bKn/5eiHUkoI/4hkP7yFh/4Sgx5ToJDW+oQh9FHR3aFK8YvH+kAaPx9Zli4Yfz9jT9rrrlmm/4yL/zOtXFFtEsHoDx/hfdp/B2OX+DiF7ekfHkrvBWVuyJ+R0o/f/E7hgBU4X0R/wEb/zESv5tKhQnEV1vFqXwxxX/M+R0uzC/mTv87x98fhdvF73/x+3NM8d+StfjvrMJVCrsivjol/j3s+SvMLeaOAfh0ADT+3cuwcMP4Oxy/R8ff3WoZhPHqq6+m6hr/zCk2xb8vS/G8pAOg0a4U5RXbniRcl6gAaAS54YYbwn777RcmTpyY8sl+T1WMWN92221h6623ztjFP0xuvfXWzH7cOP7440Ncpb2t6YUXXggPPPBAqpg46jSuMB+/wOWm9Et54/EYoErniV/u/AMjV8s+AQIECBAgQIAAAQIECBAgQKD2BOL/LEivH1N7ravuFiUuABoXQho1alQ48cQTwxNPPJEZsRVHg8aV14844ohG4jF/HE0Y/69ejGhfdtll4dxzz22Up9id5557LnNpnIYff1pKJ598cibLtddem5rGnzlggwABAgQIECBAgAABAgQIECBAoCYFJkyYYMHOhPZs4gKg0alPnz7hoYceSgU1x4wZEwYMGBA22GCDvIRxKuvRRx8dtt9++3D44YenRmnmzeggAQIECBAgQIAAAQIECBAgQIAAgTIJxBiWlEyBRAZA01RxZGe+1d7T59OfN998c3qzpJ/x3q15R8Kzzz4bJk+enLr3IYcckrlm/fXXL2l9FEaAAAECBAgQIECAAAECBAgQIJA8gbjw0aeffpq8iqlRSiDRAdBK99Guu+4a4k9L6eOPP84EQONK9AMHDmzpEucJECBAgAABAgQIECBAgAABAgRqRCAufiQlV6BDcqumZgQIECBAgAABAgQIECBAgAABAgSSLRAHxk2dOjXZlazz2rX7CNCzzjorvPTSSxn24cOHh7haekxx0aNLLrkkc66tG3FqukSAAAECBAgQIECAAAECBAgQIECgXAJGf5ZLtnTltnsAdOzYsWHkyJGZFqxYsSKzPWPGjEbnMidsECBAgAABAgQIECBAgAABAgQIEEiYwKxZs8IHH3yQsFqpTq6AKfC5IvYJECBAgAABAgQIECBAgAABAgQItELA6M9WICUgS7uPAP3Od74TBg8enGl6XOk9nbbeeutw3nnnpXer5vOXv/xliD8SAQIECBAgQIAAAQIECBAgQIBAfQjMnTs3TJw4sT4aW+WtbPcA6PHHH98s2bbbbhvij0SAAAECBAgQIECAAAECBAgQIEAgyQJjxowJq1atSnIV1e3/BEyB9ygQIECAAAECBAgQIECAAAECBAgQKEBg4cKF4a233irgClkrKSAAWkl99yZAgAABAgQIECBAgAABAgQIEKg6gTfeeCNkL+xddQ2oswpXfQB0yZIlIa4sP3/+/DrrOs0lQIAAAQIECBAgQIAAAQIECBBob4GlS5emYlHtfV/3K14g0QHQGTNmhKeffjpv6+67776w3377hd69e4dtttkm9OzZMxx00EEhRuAlAgQIECBAgAABAgQIECBAgAABAuUQiAPxli1bVo6ilVkmgUQGQEePHh0OOeSQsMEGG4QLLrigSdOfeuqpMGzYsDB8+PCwePHi1Pn40tlHH3007LzzzuGxxx5rco0DBAgQIECAAAECBAgQIECAAAECBNoisHz5coPv2gJYoWvbfRX4ltr54osvhn322ScsWrQolfW9995rdMknn3wSvv3tbzf7noV4XTz/zjvvhP79+ze61g4BAgQIECBAgAABAgQIECBAgACBYgVee+21TMyq2DJc1/4CiRsBevrppzd6kNZYY43w+eefZ2RuuummMGvWrMz+qaeeGmLQ9I477ggDBgxIHY/vA73ooosyeWwQIECAAAECBAgQIECAAAECBAgQaIvAggULwquvvtqWIlxbIYFEBUBff/318PLLL6counbtGm6//fYwZcqUEIOg6XTnnXemN8Oee+4Zrr/++rDjjjuGo446KowaNSp07949dX7EiBGZfDYIECBAgAABAgQIECBAgAABAgQItEXg+eefbzRIry1lubZ9BRIVAB03blym9RdeeGE4+uijQ0NDQ+bYxIkTU1Pb0wdOOumk9Gbqc8MNNwwnnHBCanvChAlh4cKFjc7bIUCAAAECBAgQIECAAAECBAgQIFCowNSpU0OMNUnVKZCoAOj48eMzirnBzXgie3GjDh06hH333TeTP72x3XbbpTZXrlzppbRpFJ8ECBAgQIAAAQIECBAgQIAAAQJFCcSFt5999tmirnVRMgQSGQDt0aNH6Nu3bxOh7ADo4MGDQ58+fZrkyV74KDug2iSjAwQIECBAgAABAgQIECBAgAABAgRaEHjrrbfCjBkzWsjldJIFEhUA/eyzz5q1WrJkSch+r2e+0Z/x4uwHslevXs2W5wQBAgQIECBAgAABAgQIECBAgACB1QksW7YsjB49enVZnKsCgUQFQAcNGpQii+/ujCu5Z6cY/Fy8eHHmUHMB0OxRnxtttFEmvw0CBAgQIECAAAECBAgQIECAAAEChQjExboXLVpUyCXyJlAgUQHQTTfdNEP08MMPZ7bjRlwRPp169+4dvvrVr6Z3M59xBOlNN92U2R84cGBm2wYBAgQIECBAgAABAgQIECBAgACB1grMmTMnvP76663NLl+CBRIVAD3ggAMyVOedd16IoznjYka33XZbowDosGHDQseOHTN548asWbPCYYcdFuLDGdPuu+8e+vXrl9r2HwIECBAgQIAAAQIECBAgQIAAAQKFCDz33HOpuFQh18ibTIFEBUC32WabzMruU6ZMCVtttVVYZ511wnHHHZfR69q1azjllFMy+x9++GH47ne/GzbffPPw9NNPZ45fcMEFmW0bBAgQIECAAAECBAgQIECAAAECBFor8MEHH4TJkye3Nrt8CRdIVAA0Wt1www1hs802y7DNmzcvs93Q0JAaDbr11ltnjsXFkW699dYwe/bszLHjjz8+DBkyJLNvgwABAgQIECBAgAABAgQIECBAgEBrBFatWhXi6E+pdgQSFwCNCyGNGjUqHHzwwaFbt24Z6Tga9MYbbwxHHHFE5ljciPk7d+6cOtahQ4dw+eWXh1tuuaVRHjsECBAgQIAAAQIECBAgQIAAAQIEWiPw7rvvNhpo15pr5Em2QKckVq9Pnz7hoYceCsuWLQtjxowJAwYMCBtssEHeqsZ3gR599NFh++23D4cffnjo379/3nwOEiBAgAABAgQIECBAgAABAgQIEGhJ4JVXXmkpi/NVJpDIAGjaMI7szLfae/p8+vPmm29Ob/okQIAAAQIECBAgQIAAAQIECBAgUJTA+++/n1pou6iLXZRYgcRNgU+slIoRIECAAAECBAgQIECAAAECBAjUtMDLL79c0+2r18YJgNZrz2s3AQIECBAgQIAAAQIECBAgQIBARiCu/D5jxozMvo3aEWj3KfBnnXVWeOmllzKCw4cPzyx29MQTT4RLLrkkc66tG88++2xbi3A9AQIECBAgQIAAAQIECBAgQIBAHQhkx6vqoLl11cR2D4COHTs2jBw5MoO8YsWKzHaMsmefy5ywQYAAAQIECBAgQIAAAQIECBAgQKBMAh9++GGYNm1amUpXbKUFTIGvdA+4PwECBAgQIECAAAECBAgQIECAQEUFvPuzovxlv3m7jwD9zne+EwYPHpxpWFzpPZ223nrrcN5556V3fRIgQIAAAQIECBAgQIAAAQIECBAoq8Ann3wSPv7447LeQ+GVFWj3AOjxxx/fbIu33XbbEH8kAgQIECBAgAABAgQIECBAgAABAu0h4N2f7aFc2Xskagr8kiVLwq677hp+9atfhSlTplRWxt0JECBAgAABAgQIECBAgAABAgRqWmD69Okhvv9Tqm2BRAVA//SnP4Xnn38+XHDBBWHjjTcO48ePr219rSNAgAABAgQIECBAgAABAgQIEKiYgHd/Voy+XW+cqADoiBEjMo3v27dv2HLLLTP7NggQIECAAAECBAgQIECAAAECBAiUSmDmzJlh0qRJpSpOOQkWSFQAND546RSnwjc0NKR3fRIgQIAAAQIECBAgQIAAAQIECBAomYDRnyWjTHxBiQqADhkyJAP2wgsvhFWrVmX2bRAgQIAAAQIECBAgQIAAAQIECBAohcCcOXPC+++/X4qilFEFAokKgA4dOjSstdZaKbaPP/443HHHHVVAqIoECBAgQIAAAQIECBAgQIAAAQLVJPDaa68ZeFdNHdbGuiYqALrJJpuExx9/PGy44YapZh177LHhe9/7XnjjjTfCwoUL29hUlxMgQIAAAQIECBAgQIAAAQIECNS7wIIFC8Lbb79d7wx11f5OSWptHHocH8BzzjknXH755eGTTz4Jt9xyS+on1rN///5h0003DWuvvXarqv3oo4+2Kp9MBAgQIECAAAECBAgQIECAAAEC9SEQB9qtWLGiPhqrlSmBRAVAR48eHU444YRmu2bq1Kkh/kgECBAgQIAAAQIECBAgQIAAAQIEChVYunRpePPNNwu9TP4qF0jUFPgqt1R9AgQIECBAgAABAgQIECBAgACBBAuMHTs2LFu2LME1VLVyCCRqBOjmm28eTjvttHK0U5kECBAgQIAAAQIECBAgQIAAAQJ1LLB8+fLUOjN1TFC3TU9UAHTw4MEh/kgECBAgQIAAAQIECBAgQIAAAQIESinw1ltvhUWLFpWySGVViYAp8FXSUapJgAABAgQIECBAgAABAgQIECBQnMCqVavCa6+9VtzFrqp6gaoPgE6YMCHEIcwSAQIECBAgQIAAAQIECBAgQIAAgXwCEydODHPnzs13yrE6EEhsADQOSb799tvDN7/5zXDTTTc12xXf/e53Q9++fcNxxx0XXnjhhWbzOUGAAAECBAgQIECAAAECBAgQIFCfAmPGjKnPhmt1SiCRAdDJkyeHHXfcMRx77LHhb3/7Wxg3blyz3TVp0qQwZ86ccNttt4XddtstXH311c3mdYIAAQIECBAgQIAAAQIECBAgQKC+BD744IMwc+bM+mq01jYSSFwA9O233w477bRTo6BnPJYvLVmypNEDHKfC/+QnPwk//vGP82V3jAABAgQIECBAgAABAgQIECBAoM4EjP6ssw7P09zEBUDPPffcMGPGjFRVGxoawhFHHBHOPPPMPFUPoWvXrqm8d999d2rEaDrTtddeG15++eX0rk8CBAgQIECAAAECBAgQIECAAIE6FJg+fXr46KOP6rDlmpwtkKgA6EsvvRQefvjhVP26d+8eHnzwwXDvvfeGffbZJ7vOjbbXXXfdcOSRR4bnn38+nHbaaalzcWWvM844o1E+OwQIECBAgAABAgQIECBAgAABAvUlEGNNEoFEBUCHDx+e6ZHzzz8/HHzwwZn9ljY6deoUrrrqqrDlllumso4ePTp8+umnLV3mPAECBAgQIECAAAECBAgQIECAQA0KxHd/xnVmJAKJCoC+9957mR45+eSTM9ut3VhjjTVSCyel87/11lvpTZ8ECBAgQIAAAQIECBAgQIAAAQJ1IrBy5cowcuTIOmmtZrYkkKgAaFzRPaa11lor9OnTp6W65z2fHgEaT77zzjt58zhIgAABAgQIECBAgAABAgQIECBQuwKvv/56mDNnTu02UMsKEkhUAHTttddOVX7+/PmNVncvpEXTpk3LZO/Xr19m2wYBAgQIECBAgAABAgQIECBAgEDtCyxcuNDi2LXfzQW1MFEB0I022ihT+VdeeSWzXchG9nXbbLNNIZfKS4AAAQIECBAgQIAAAQIECBAgUOUCo0aNCsuWLavyVqh+KQUSFQDdZZddMm0777zzwtKlSzP7rdkYN25cuOuuu1JZe/XqFQYOHNiay+QhQIAAAQIECBAgQIAAAQIECBCoAYE4M/jtt9+ugZZoQikFEhUAPeyww8K6666bal98V0NcCCkOW25NmjJlSjjiiCMy+Q888MDWXCYPAQIECBAgQIAAAQIECBAgQIBAjQg8++yzNdISzSilQKICoF26dAlnnXVWpn233npr2GSTTcJvfvObVPR+0aJFmXNx49NPPw1xyvv3v//9sOmmm2Yi/P/yL/8SrrnmmkZ57RAgQIAAAQIECBAgQIAAAQIECNSuwPjx48P06dNrt4FaVrRAp6KvLNOF5557bhg9enR4+OGHU3eYOXNmOOecc1I/8UAcIdqzZ88wderUsHjx4ry1uPnmm0Pv3r3znnOQAAECBAgQIECAAAECBAgQIECgtgTiOz9jPEkikE8gUSNAYwUbGhrC7bffHk466aTUdm6l46jP999/P2/wc9CgQeGBBx4I++67b+5l9gkQIECAAAECBAgQIECAAAECBGpU4MUXXwy5M4drtKmaVYRA4gKgsQ1rrbVWuPHGG8PTTz8dvvWtb4UePXqstmlxwaPLL788xKHOMb9EgAABAgQIECBAgAABAgQIECBQHwJz5swJ//jHP+qjsVpZlEDipsBnt2KPPfYI8WfJkiXh+eefDx999FHqXQ4LFiwIG2+8ceq9n/Hdn/379887WjS7LNsECBAgQIAAAQIECBAgQIAAAQK1JzBixIiwcuXK2muYFpVMINEB0HQru3btGvbee+/0rk8CBAgQIECAAAECBAgQIECAAAECqdnAH374IQkCqxVI5BT41dbYSQIECBAgQIAAAQIECBAgQIAAgboXmD9/fhg5cmTdOwBoWUAAtGUjOQgQIECAAAECBAgQIECAAAECBBIm8OSTT4a4+rtEoCUBAdCWhJwnQIAAAQIECBAgQIAAAQIECBBIlEBc9MjU90R1SaIrIwCa6O5ROQIECBAgQIAAAQIECBAgQIAAgWyBuXPnhlGjRmUfsk1gtQICoKvlcZIAAQIECBAgQIAAAQIECBAgQCApAqtWrQp///vfw+eff56UKqlHFQgIgFZBJ6kiAQIECBAgQIAAAQIECBAgQIBACK+//nr45JNPUBAoSEAAtCAumQkQIECAAAECBAgQIECAAAECBCohMGfOnPDCCy9U4tbuWeUCAqBV3oGqT4AAAQIECBAgQIAAAQIECBCodYE49f2JJ54Iy5cvr/Wmal8ZBDqVoUxFlkAg/mLXWsptU+5+rbW3HO1Jm6U/y3GPWi0z1yx3v1bbXcp2ZZtlb5fyHrVcVrZZ9nYtt7mUbUubxc/0dinLr6eyGLattz1/hfvlmuXuF15i/V2RNkt/1p9AaVoc/RgWb8mucLtcs9z9wkusvyvSZvHz1VdfDdOmTasLhNjedNtL1eBSl9eWejU0NLTl8qKubfgnQO1F2oqiSNZF06dPDytXrkxWpdSGAAECBAgQIECAAAECBAgQINDOArNnzw6PPPJI3cRJBg0aFPbYY492Vm6f23Xv3j307NmzfW6WdRcjQLMwkrS59tprlzzaX+n2xWHqCxcuTFVjjTXWCPGhlwoTWLp0aViyZElF/rAorKbJyx3/X8+8efNSFevYsWNYc801k1fJhNco/Tsc7aKhVJhAfP7S/8+xEn/hF1bb5OWO/1Nw/vz5oVu3bqFz587Jq2DCaxT//k1PF/M7XFxnxd/h+Ox17dq1uALq+Kr43SV+h4kpfv+L3wOlwgTS36F79OhR2IVyp1aJXrRoUUqiS5cufoeLeCbi7/CyZctC/DeqVJjAihUrwoIFC1IXderUKfgdLswv5o7PXjR86aWX6uo7YKmChHPnzk2hxxGXSfkdjr8LlUiVuWslWlpl94z/wKu1FP/gSn95iw+8AGjhPRyDJ/ELCLvC7WLwJB0A7dChA8PCCVP/eI2/w/Ef//7xWjhg/OIWvwTH5He4cL90ADT+47UW/44sXKSwK+LfHekAaPSr1BfP/8fefYBHWaUNH78pIQQIAZGmgHQQZAUFBcSGNLGAugisqOC6oOLaVvls77rCsrvu6isWVBArKGKjN4GggJSEHprUQKihl5BOvvc+ez3PNUMSyCSTyZmZ/7muyZx56jm/Z2Yyc88pvpXarq01AM/nl8JdE33vcwKgGkTmNey7o76GNfH/w3e71NRUcQKgNMLw3U/30P8fmZmZPP8Kwad2TgBUGxDwGvYdUQN3v/zyi/khPJy+g+hnXn88X5wAKN+BRawKgOpYDhs2bPD9FeGxh76paMsa51a1alVaKnn4kEUAAQQQQAABBBBAAAEEEEAAAQSCQWDv3r2yadMmWs8Gw8WyvIxWBUAXLFggAwYM8CtZdHS03HrrrdKjRw/54x//GFZNpv0KycEQQAABBBBAAAEEEEAAAQQQQACBAAlo74GFCxe6w0gF6LScJkQFSodovdxqaXeladOmyRNPPCHXXXedbN682V1HBgEEEEAAAQQQQAABBBBAAAEEEEDAPoHY2Fh3CAH7SkeJgk3AqgBojRo15MYbb5R27drl6ajjLjVo0ECaNWtWqLGD1q1bJ927d5ejR4/meXwWIoAAAggggAACCCCAAAIIIIAAAgiUrMCWLVtk+/btJVsIzh5SAlYFQLt27SqLFi2S5s2bu8jVq1eXUaNGSWJiopn8ZefOnaIvBB3IOjk5WaZPn+4VMG3VqpXs2bNHdJyIjRs3mtafffr0cccBTUpKkiFDhrjHJ4MAAggggAACCCCAAAIIIIAAAgggYIeATl6rEx+REPCngFUBUK3Yf/7zHxk/frypY79+/UQDnk8//bRcccUVbhDTAdDg6J133ilxcXEybtw4szghIUH+8Y9/yOWXXy4tWrSQu+66S7799lv54YcfRGfR0jR58mTRQCgJAQQQQAABBBBAAAEEEEAAAQQQQMAOgZycHJk3b55kZGTYUSBKETICVgVAd+zYIS+++KLBbd26tXz66adSqVKlAmHrBEcaKNX00UcfmZahnjv26tXLXX/u3Dn5+uuvPVeTRwABBBBAAAEEEEAAAQQQQAABBBAoQYHVq1fL/v37S7AEnDpUBawKgC5ZskQ0OKlp5MiRPo/z+frrr0upUqXM/nPmzDH3nn8ef/xx9+HatWvdPBkEEEAAAQQQQAABBBBAAAEEEEAAgZIT0GEOly9fXnIF4MwhLWBVAHTZsmUGW4OY7du39xk+JiZGmjZtavZbsWJFrv3r16/vdoPnF4VcPCxAAAEEEEAAAQQQQAABBBBAAAEEAi6QlZVlur47jeICXgBOGPICVgVAN2/ebMAjIiIkOjq6UPhOl/mtW7fmub+OG6rp2LFjea5nIQIIIIAAAggggAACCCCAAAIIIIBA4AS0QRxxmsB5h+OZrAqA6sRFmnSw28J0UU9PT5f169ebY+gESOcnHUxXm1Rrql279vmreYwAAggggAACCCCAAAIIIIAAAgggEEAB7aG7bt26AJ6RU4WjgFUB0EaNGrnXYMyYMW6+oBmdPT4zM9Ns3rZt21y7aQtTZyYxnVWehAACCCCAAAIIIIAAAggggAACCCBQMgLa9X3BggWiDdZICBSngFUB0P79+7uTGH3yyScybty4AtddW4w+9dRT7vZ5BUDffPNNd32zZs3cPBkEEEAAAQQQQAABBBBAAAEEEEAAgcAKaNf3EydOBPaknC0sBawKgGq39TvvvNO9EH/605+kd+/ekpCQ4C47P6NNpZ9++mnp0KGDpKammtVt2rSR+++/391048aNMnToUPnss8/MsqioKHn44Yfd9WQQQAABBBBAAAEEEEAAAQQQQAABBAInQNf3wFlzJpGytiGMHTtWOnXqJDt27DBFmzp1qkybNk3q1KkjOou73jTt2bNHkpKSzL02mXZSjRo1RPepUKGCWaRd4lu3bi2e2zz66KPiTIbk7Mc9AggggAACCCCAAAIIIIAAAggggEDxC9D1vfiNOYO3gHUB0Fq1aslPP/0kd999t2jLTU06FoQGO/W2ePFi7xp4PLrsssvku+++k7p167pLd+3a5RX87NGjh4wYMcJdTwYBBBBAAAEEEEAAAQQQQAABBBBAIHACdH0PnDVn+q+AVV3gnYvSsGFDMwv822+/LTVr1nQW53uvrTnfeust02q0Y8eOXttt3bpVIiMjTSvQv/3tbzJz5kyJiYnx2oYHCCCAAAIIIIAAAggggAACCCCAAALFL0DX9+I35gy5BaxrAeoUsWzZsvLMM8+YiY1WrFghs2fPlsTERDl48KCZ6b1x48bSpEkTadq0qXTp0kUqVark7Op137lzZ0lJSZEyZcp4LecBAggggAACCCCAAAIIIIAAAggggEDgBOj6HjhrzuQtYG0A1Clm6dKlzQRHOslRYZIzFmhh9mUfBBBAAAEEEEAAAQQQQAABBBBAAAH/CND13T+OHMV3ASu7wPteDfZAAAEEEEAAAQQQQAABBBBAAAEEELBVgK7vtl6Z8CgXAdDwuM7UEgEEEEAAAQQQQAABBBBAAAEEECgRgczMTFmwYIGZ5LpECsBJw17A+i7wR48elSNHjkhGRobXbO4FuXJt2rQpyGZsgwACCCCAAAIIIIAAAggggAACCCBQTAI6t8uJEyeK6egcFoGLC1gZAD19+rR8+eWXMnr0aNm8efPFa5HPFjk5OfmsYTECCCCAAAIIIIAAAggggAACCCCAQHELJCcny9q1a4v7NBwfgQsKWBcA1Rnbr7vuOtmyZcsFC85KBBBAAAEEEEAAAQQQQAABBBBAAAF7BbRhWmxsLF3f7b1EYVMy68YAfeyxxwh+hs3Tj4oigAACCCCAAAIIIIAAAggggECoCqxevVoOHz4cqtWjXkEkYFUL0Li4OJkwYYLLV6VKFRkxYoR06NBBqlWrJlFRUVKqVCl3PRkEEEAAAQQQQAABBBBAAAEEEEAAAfsETp48KRrnISFgg4BVAdD4+HjXpF69ejJ37lxp3ry5u4wMAggggAACCCCAAAIIIIAAAggggID9AgsXLvR5Mmv7a0UJg1XAqi7w2jTaSU888QTBTweDewQQQAABBBBAAAEEEEAAAQQQQCBIBHRC66SkpCApLcUMBwGrAqCJiYmueefOnd08GQQQQAABBBBAAAEEEEAAAQQQQAAB+wXOnj0rS5Yssb+glDCsBKwKgF599dUufq1atdw8GQQQQAABBBBAAAEEEEAAAQQQQAAB+wUWL14saWlp9heUEoaVgFUB0I4dO7r4GzdudPNkEEAAAQQQQAABBBBAAAEEEEAAAQTsFtCevVu3brW7kJQuLAWsCoDqbO9O+uqrr5ws9wgggAACCCCAAAIIIIAAAggggAACFgtkZGTIzz//bHEJKVo4C1gVAL388svl5ZdfNtdjwoQJZhb4cL441B0BBBBAAAEEEEAAAQQQQAABBBAIBoHly5fL6dOng6GolDEMBawKgKr/yJEjZfDgweZS9OzZU4YMGSJ79uyRnJycMLw8VBkBBBBAAAEEEEAAAQQQQAABBBCwW+DQoUOyfv16uwtJ6cJaoKxNtddxIubPny+tWrWS6tWry+HDh2Xs2LHmFhkZKfXq1ZPLLrtMSpcuWNw2NjbWpupRFgQQQAABBBBAAAEEEEAAAQQQQCCkBLKzs00sh4ZrIXVZQ64yVgVA4+PjZejQoXkip6eny7Zt28wtzw1YiAACCCCAAAIIIIAAAggggAACCCAQUIEVK1bIsWPHAnpOToaArwIFa0rp61HZHgEEEEAAAQQQQAABBBBAAAEEEEAgpAW06/vq1atDuo5ULjQErGoB2qJFCxk2bFhoyFILBBBAAAEEEEAAAQQQQAABBBBAIEQF6Poeohc2RKtlVQC0TZs2ojcSAggggAACCCCAAAIIIIAAAggggIC9AnFxcXR9t/fyULLzBOgCfx4IDxFAAAEEEEAAAQQQQAABBBBAAAEE8hdITk6m63v+PKyxUIAAqIUXhSIhgAACCCCAAAIIIIAAAggggAACNgo4Xd/PnTtnY/EoEwJ5ChAAzZOFhQgggAACCCCAAAIIIIAAAggggAAC5wvEx8fL0aNHz1/MYwSsFgj4GKDPP/+86DgRTpo7d65ERUWZh/PmzZMRI0Y4q4p8v2jRoiIfgwMggAACCCCAAAIIIIAAAggggAACCIho1/dVq1ZBgUDQCQQ8AJqQkCCLFy92obTptJP0heS5zlnOPQIIIIAAAggggAACCCCAAAIIIIBAyQlol/cFCxYIXd9L7hpw5sIL0AW+8HbsiQACCCCAAAIIIIAAAggggAACCISFgHZ9P3LkSFjUlUqGnkDAW4D2799f2rZt60qWK1fOzV911VXy8ssvu4/JIIAAAggggAACCCCAAAIIIIAAAgiUrMDhw4dl5cqVJVsIzo5AEQQCHgAdOHBgvsW9+uqrRW8kBBBAAAEEEEAAAQQQQAABBBBAAIGSF9Au7/Pnz6fre8lfCkpQBAG6wBcBj10RQAABBBBAAAEEEEAAAQQQQACBUBbQlp90fQ/lKxwedQvZAChNs8PjCUwtEUAAAQQQQAABBBBAAAEEEECgeASOHz9O1/fioeWoARawKgAaFxdX5F8VMjIy5NVXX5X27dsHmJLTIYAAAggggAACCCCAAAIIIIAAAqEhkJOTY7q+Z2dnh0aFqEVYC1gVAN22bZt07dpV9BeGwqTVq1ebCZZGjhwpvEALI8g+CCCAAAIIIIAAAggggAACCCCAgMj69evl4MGDUCAQEgJWBUBVdO3atdK9e3c5depUgYEzMzPlr3/9q1x//fWSkJBQ4P3YEAEEEEAAAQQQQAABBBBAAAEEEEDAW+D06dOyfPly74U8QiCIBawLgKplfHy83H777XLmzJmL0mrAtG3btjJixAjJyspyt7/qqqvcPBkEEEAAAQQQQAABBBBAAAEEEEAAgYIJxMbGig4xSEIgVASsCoDWr19fypQpY2yXLl0qd911l6SmpuZpra0+//a3v0m7du1Ms2xno3Llysnrr78u2h2ehAACCCCAAAIIIIAAAggggAACCCBQcIEtW7bInj17Cr4DWyIQBAJWBUBvuOEG+frrr6Vs2bKG7ueff5ZevXpJenq6F+W6devkuuuuM4FOz1afOvHRmjVrTHf4iIgIr314gAACCCCAAAIIIIAAAggggAACCCCQv8DZs2dl8eLF+W/AGgSCVMCqAKga3n///fLNN9+IE8CcN2+e3HfffabptQY7hw8fblp9atd3J1WsWFFGjRolv/76q7Ro0cJZzD0CCCCAAAIIIIAAAggggAACCCCAQAEFFi1aJGlpaQXcms0QCB6B/za1tKy8GvD89ttvTTBUu7rPnDlT7rnnHjlw4IBp4elZ3G7dusmYMWNEu8+TEEAAAQQQQAABBBBAAAEEEEAAAQR8F9i5c6ds27bN9x3ZA4EgELCuBahj1rt3b/nhhx9Ex/TUNGvWLK/g5yWXXCKff/65zJ07l+Cng8Y9AggggAACCCCAAAIIIIAAAggg4KOATnikwxCSEAhVAWsDoAqukyBNnjxZIiMjvfy1heimTZvk4Ycf9lrOAwQQQAABBBBAAAEEEEAAAQQQQAAB3wR0SMGUlBTfdmJrBIJIwOoAqDr27NlTpkyZIuXLl3dZNSBavXp19zEZBBBAAAEEEEAAAQQQQAABBBBAAAHfBfbt2ycbNmzwfUf2QCCIBKwPgKpljx49ZNq0aRIVFWVodab4wYMHS05OThBRU1QEEEAAAQQQQAABBBBAAAEEEEDAHgGddyU2NtaeAlESBIpJIOCTID3//PMSFxdXqOrExMRIamqq2feTTz6RVatWSXR0dL7H0tnLSAgggAACCCCAAAIIIIAAAggggAACuQV03M8TJ07kXsESBEJMIOAB0ISEBFm8eLFfGNeuXeuX43AQBBBAAAEEEEAAAQQQQAABBBBAIJwEND6zZcuWcKoydQ1jgaDoAh/G14eqI4AAAggggAACCCCAAAIIIIAAAn4VOHTokN8ap/m1YBwMgWISCHgL0P79+0vbtm2LqTocFgEEEEAAAQQQQAABBBBAAAEEEEAgP4G0tDSZM2eOZGdn57cJyxEIOYGAB0AHDhwYcohUCAEEEEAAAQQQQAABBBBAAAEEEAgGgXnz5smpU6eCoaiUEQG/CdAF3m+UHAgBBBBAAAEEEEAAAQQQQAABBBCwV2DlypWSmJhobwEpGQLFJEAAtJhgOSwCCCCAAAIIIIAAAggggAACCCBgi0BSUpIsX77cluJQDgQCKkAANKDcnAwBBBBAAAEEEEAAAQQQQAABBBAIrEBKSorMnTtXcnJyAntizoaAJQIBHwPU13ofPXpUjhw5IhkZGZKVleXT7m3atPFp+4JsvGjRIjNY8O7du+XgwYMSExMjDRo0kK5du5pbmTJlCnIYtkEAAQQQQAABBBBAAAEEEEAAAQSKXeDcuXMye/ZsSU1NLfZzcQIEbBWwMgB6+vRp+fLLL2X06NGyefPmQtv585eNY8eOyWuvvSZr1671Ks/hw4dFb3FxcbJw4UIZPny4REZGem3DAwQQQAABBBBAAAEEEEAAAQQQQKAkBH799Vc5cOBASZyacyJgjYB1AVBtln3dddfJli1brEHSlqevvvqqJCQkmDLVqFFD7rjjDqlTp44JfuoMajt27JClS5fKs88+K2+88YZER0dbU34KggACCCCAAAIIIIAAAggggAAC4Sewffv2XA25wk+BGiMgYt0YoI899phVwU99knz22Wdu8POaa66R8ePHyyOPPCLdunWTBx54QMaNGyfdu3c3zycNkn700Uc8txBAAAEEEEAAAQQQQAABBBBAAIESEzh+/LgsWLCgxM7PiRGwScCqFqDajXzChAmuT5UqVWTEiBHSoUMHqVatmkRFRUmpUqXc9YHIaDd6HStDk3Zt15agFSpU8Dp12bJl5bnnnhNtVn7mzBkzRujTTz8t5cqV89qOBwgggAACCCCAAAIIIIAAAggggEBxC2RmZsqsWbPMfCrFfS6Oj0AwCFgVAI2Pj3fN6tWrZ2Yoa968ubusJDKbNm0y3dz13Ndee61Ur149z2JoULRx48amablO2KQTJGkdSAgggAACCCCAAAIIIIAAAggggEAgBWJjY0XnMiEhgMB/BawKgK5evdq9Lk888YSUdPBTC9OyZUuZOnWqGeOzcuXKbvnyypw9e9YsLl26tGjrVRICCCCAAAIIIIAAAggggAACCCAQSIF169bJ1q1bA3lKzoWA9QJWBUATExNdsM6dO7v5ks5ccsklorcLJf1lRSdC0qRB04sFSy90LNYhgAACCCCAAAIIIIAAAggggAACvgrobO9LlizxdTe2RyDkBayaBOnqq692wWvVquXmbc/owMIvvPCCZGdnm6J27drV9iJTPgQQQAABBBBAAAEEEEAAAQQQCCEB7ZU6Z84cOXfuXAjViqog4B8Bq1qAduzYUd5++21Ts40bN0rdunX9U8tiOMratWtlz5498ttvv5mxStPT06VMmTKikx/dc889RT5jVlaW6ARMoZS0Tk7SN2QdlJnkm4ATZMfONzfd2vNDgL62MPTd0HkNO/e+HyG89/B8T+f55/tzwXkN6/sgfr77eT7/QvEzhu8ivu+hhnx+8d1N93A+vzh5XsO+Ozrvgdj5bsfzz3ez8/fQ5x+fn89XKdhjz8/NxW2ox9dJj06ePFmwwgXJVlovTfpaDvSk2CVJ5O/PvMX9/PPFSoeN1PhZoFOp/0OwJsq2b98+qVOnjjEYMGCAjB8/PtAeBT5f79695ejRo17bP/roo/Lwww97LSvsg0OHDnkFbAp7HPZDAAEEEEAAAQQQQAABBBBAAIHQFli5cqUkJCSEdiXDqHYNGzaUm2++OSRrrJOIx8TEBLxuVnWBv/zyy+Xll182CBMmTDAtKwMuUoATamtPDX7quKBRUVHuHuPGjZOHHnrIHQvUXUEGAQQQQAABBBBAAAEEEEAAAQQQKAaB3bt3E/wsBlcOGVoCVrUAdWiHDBkiY8eOFW0Wq60qX3nlFdMd3pbmztoUOTU1VSpVqmS6AmhX+Pfee09WrFhhqqDL33//fWnUqJFTJZ/vtdm609XF550t3UHrk5GRYUqnzZ0jIiIsLam9xdIuFHorX768vYW0tGTa2F1/vNCk7yWRkZGWltTeYjmv4XLlypn3Z3tLamfJ0tLS3ILxGnYpCpxxXsP6v6MkuswUuKCWbqj/f53PFbyGC3eR9DXM55fC2Wm3bacbMq/hwhk6n6H19UvyTcCzGymvYd/snK2d1zCfXxyRgt87n591D41vFMdrWGMHU6ZMcb9rF7x0wbGlfgbU13HZslaN4FjseBpP8sfk4M53EJu+A+t3cW0FGuhkVQB069atMn/+fPMBffjw4XL48GHXQ4Hq1asnl112WYG/eMfGxrr7ByLz+uuvm/LruTp06CD//ve/A3HaoDmHfnBzhg3QlrNVqlQJmrLbUtCUlBQ5deqU1K5d25YiBU059MOHDi2hSb98XXrppUFTdlsKqgHkY8eOGTt+wPD9qiQnJ7sBAF7Dvvs5r2H93+HZ+8L3I4XnHvradX4Eql69eth9ifDHVdf/Ifrcq1y5sj8OF1bHOH36tJw5c8bUmddw4S69voY1aQ80km8C2nDlxIkTZidtqBIdHe3bAdjafP9Qx5o1a6Lho4A2XnHiGhrT8PdrWIPT3333nfs928fiBcXmWkcN4unr15ZGcYGAa9q0qXTv3r3Ipzpw4IA5hv4AVKNGjSIfL5gPYFUIPT4+XoYOHZqnp35o37Ztm7nluYEFC5999llZvHix+YKxbNkyOXLkCEEWC64LRUAAAQQQQAABBBBAAAEEEEAg1AQWLlwY0sHPULte1KdkBawaA7RkKYp+dm0R4NntXSd1IiGAAAIIIIAAAggggAACCCCAAAL+FFi/fr389ttv/jwkx0IgpAWsagHaokULGTZsmFXg2l1HA5n79++Xq6+++qJN1qtWreqWX7sJkBBAAAEEEEAAAQQQQAABBBBAAAF/CWi3Zu19SkIAgYILWBUAbdOmjejNpjRp0iT5/PPPTZG0e36/fv0uWLxdu3a565s0aeLmySCAAAIIIIAAAggggAACCCCAAAJFEdBGWrNmzXInNyzKsdgXgXASoAv8Ra52u3bt3C0WLFjg5vPKbNy40bQU1XXVqlUzt7y2YxkCCCCAAAIIIIAAAggggAACCCDgi4BOqqTBz7Nnz/qyG9sGmcDu3bslJycnyEptf3EJgF7kGmm3fGe26C1btpgZ1vLaRWe3/Mc//uGuulhLUXdDMggggAACCCCAAAIIIIAAAggggMBFBHTSo0OHDl1kK1YHs8C6devk7bfflv/93/+V7OzsYK6KdWUP2QBoSkqKX7DLli0rf/vb36R06f9SjR49Wt5//333Tef48eMSGxsrDz74oOzZs8ecs23bttK3b1+/nJ+DIIAAAggggAACCCCAAAIIIIBAeAusXbtWtFEWKXQFNm3aZIZgPHfunMybN080/kTyn4BVY4DmVa2jR4+a5t2ZmZm5mgBrk2C9aTPw9PR0cSYsmj59ukyZMsU8zuuYvi7TyY+efvppeffdd00EXscF1Vu5cuUkIyPD63Aa/Hz11VelVKlSXst5gAACCCCAAAIIIIAAAggggAACCPgqkJSUJEuWLPF1N7YPIoFt27bJJ5984rb6vO666+Txxx8PohrYX1QrA6AHDx6U9957Tz788EPRFpY2pHvvvVeuueYaeeedd2TlypWmSJ7BT+0mP2TIEOnRo4cNxaUMCCCAAAIIIIAAAggggAACCCAQ5AInT56UOXPm5GoQFuTVovgeAjqZ9pgxY0Qb/mlq2rSpaVgXERHhsRXZogpYFwDVZt033XST6JiatqX69eubsRi0pWliYqLs27dPqlevLg0aNJCqVavaVlzKgwACCCCAAAIIIIAAAggggAACQSqgAbGZM2dKWlpakNaAYl9MQFv3fvTRR27v4oYNG8rgwYNNj+OL7ct63wSsC4A+8sgjRQp+RkdHy+233y7dunXzTcKHrStVqiRXXXWVufmwG5sigAACCCCAAAIIIIAAAggggAACBRLQcSB1WEBSaAocOHBAPvjgA0lNTTUVrFu3rulZrMMtkvwvYFUAdNGiRbJmzRq3ljrmwTPPPCONGjWSvXv3yn333WfW6Ribmtem4BotnzVrlkycONGs00mL/vOf/0i9evXc45BBAAEEEEAAAQQQQAABBBBAAAEEgkUgPj5eduzYESzFpZw+Cugk2jrsozOBd+3ateWJJ56QqKgoH4/E5gUVsCoAqrOpO6lNmzaybNkyd/b1a6+9VmJiYkzQc+PGjTJixAhnUxkwYIDceOONMnToUDNmaP/+/WXx4sXuvu6GZBBAAAEEEEAAAQQQQAABBBBAAAGLBXRMyOXLl1tcQopWFAENbOuYn87QBjVq1JAnn3xSKlasWJTDsu9FBEpfZH1AV2/YsME93+uvv+4VwCxTpox06tTJrNdm4NnZ2e62mtHZsT7//HOzz9KlS+X777/3Ws8DBBBAAAEEEEAAAQQQQAABBBBAwGaBY8eOyU8//WRzESlbEQQ2bdpkur07wc/LLrtMnn76adHhHEnFK2BVANRzxvfrr78+V81btmxplukkRNu3b8+1/qGHHnLH/nzrrbdyrWcBAggggAACCCCAAAIIIIAAAgggYKNAenq6GeIvIyPDxuJRpiIK6JCPY8eOdWd714m2n3rqKYKfRXQt6O5WBUB1hjNNOuaBNgE+PzVt2tRdtH79ejfvmfnDH/5gHups8llZWZ6ryCOAAAIIIIAAAggggAACCCCAAALWCWj8YsaMGWZYP+sKR4GKLKBDPGqv5XPnzpljaXxLh3GsUKFCkY/NAQomYFUAtHHjxqbU+sLPycnJVYNmzZq5y9atW+fmPTNNmjQxD/UXk61bt3quIo8AAggggAACCCCAAAIIIIAAAghYJaBBsdmzZ8v+/futKheF8Y/AwoULzcTdTpyrVatWZrb3yMhI/5yAoxRIwKoAaPPmzU2htSVocnJyrgoUpAVopUqV3P0SEhLcPBkEEEAAAQQQQAABBBBAAAEEEEDANoH58+dLYmKibcWiPH4QmDt3rkyePNk9Utu2beWRRx6RiIgIdxmZwAhYGQDVqns+QRwK7RZfuXJl8zA+Pj7PVqK7d+92Nndn1HIXkEEAAQQQQAABBBBAAAEEEEAAAQQsEfjll1/kt99+s6Q0FMOfAlOnTpWZM2e6h9SJvR988EHRSb5JgRewKgDaokULV2DkyJGyefNm97GTad26tckePHjQjI/hLHfuJ0yY4GSlUaNGbp4MAggggAACCCCAAAIIIIAAAgggYItAXFyc5De/iS1lpBy+C2hX92+//VYWLFjg7nzbbbfJ/fffL6VKlXKXkQmsgFUBUB0DtHPnzkZg79690qFDB3nhhRe8Znzv16+fK6SzvsfGxkp2draZRetf//qXTJo0yV3vjAfqLiCDAAIIIIAAAggggAACCCCAAAIIlLCABj5XrFhRwqXg9P4W0PFcv/rqK1myZIl76DvuuEN69erlPiZTMgJWBUCV4I033pDy5csbjZMnT8qbb75pZspyeAYOHCiXXnqpeXjixAnRKLo+jo6OlpdeesntFt+7d2+pWbOmsxv3CCCAAAIIIIAAAggggAACCCCAQIkL6ITN2vWdFFoC2jhPZ3rXlr1Ouueee6R79+7OQ+5LUMC6AKgOCKtNhT2Dl55d2aOiomTUqFFeYyZoIDQ9Pd1lLFeunPz73/92H5NBAAEEEEAAAQQQQAABBBBAAAEESlpA5y2ZN29eSReD8/tZICMjQ8aOHStr1641R9au7tqD+dZbb/XzmThcYQWsC4BqRe666y7Zs2ePfPnll3LtVL+AngAAQABJREFUtdfmGsvzgQceMJMkaTD0/FS/fn1ZuHCh0P39fBkeI4AAAggggAACCCCAAAIIIIBASQkcOHBAZs+eLdpNmhQ6AikpKfLee++589iULl1adMjGjh07hk4lQ6AmZW2tg7bi1Nmx9JbXm4MGSffv3y+LFy82TcfLli0rbdq0kR49ekhMTIyt1aJcCCCAAAIIIIAAAggggAACCCAQZgLHjh0zc5hkZmaGWc1Du7p6XT/44ANJTk42FdXY1KBBg6RVq1ahXfEgrJ21AVBPS42e55WqVKliWotqMJSEAAIIIIAAAggggAACCCCAAAII2CZw+vRp+emnn9w5S2wrH+UpnMC+ffvkww8/lFOnTpkDaC/lwYMH5+rFXLijs5e/BYIiAOpUWn8p2blzp5n1vWHDhu5kSc567hFAAAEEEEAAAQQQQAABBBBAAAFbBM6cOSNz586V1NRUyWsYP1vKSTl8E9i2bZt8/PHHkpaWZnbUBnqPP/641K5d27cDsXXABKwOgObk5MikSZNk/PjxorOkJSYmSlZWlsHRAWUvv/xyady4sbRr106GDRvmzg4fMD1OhAACCCCAAAIIIIAAAggggAACCOQhoC0D58yZI9oCVLtGk0JDQCc60jlrnPhUrVq1TPCzatWqoVHBEK2Fta/AyZMny2uvvSYJCQl50mtwdO/eveb2888/m9m2XnnlFXnqqackMjIyz31YiAACCCCAAAIIIIAAAggggAACCBS3wMmTJ2XKlCkm+Fnc5+L4gRNYsWKFfP311+5wBg0aNJAhQ4ZIhQoVAlcIzlQogbwH1yzUofy30xtvvCH33ntvvsHPvM6kby7aClQnQtI8CQEEEEAAAQQQQAABBBBAAAEEEAi0wIkTJ+THH38k+Blo+GI+36pVq7yCn1dddZUMHTqU4Gcxu/vr8Na1ANXWnNqS0zPdcsstcvfdd0u9evWkbt26UrlyZdPyc/fu3bJ582b55JNPRN9gNOnjRx55RH744QfPQ5BHAAEEEEAAAQQQQAABBBBAAAEEilXg+PHjpuWnjv1JCh2BdevWmeEZtTeyprZt28qAAQMkv0m7Q6fmoVMTqwKgOi5Gv379zCRHSty6dWt57733pFOnTrnEmzdv7i77n//5H3nrrbdk5MiRcu7cOfNLi+735z//2d2GDAIIIIAAAggggAACCCCAAAIIIFBcAseOHTPBz5SUlOI6BcctAYGNGzfKZ599ZuJNenqNVRH8LIELUcRTWtUFfvny5XLo0CFTpcsuu0xmzZqVZ/Dz/DrHxMTI8OHD5R//+Ie7SgOiJAQQQAABBBBAAAEEEEAAAQQQQKC4BTT4qXOZEPwsbunAHn/Lli2m17E2ttOk3d4ffvhhWn4G9jL45WxWBUDj4uLcSr377rtSu3Zt93FBMv/v//0/ufnmm82m2j3eCaYWZF+2QQABBBBAAAEEEEAAAQQQQAABBHwVOHr0qOmJevbsWV93ZXuLBbZt2yYff/yxO9u79kQeNGiQlClTxuJSU7T8BKwKgOpsWk5yApnO44Le9+jRw93UM6DqLiSDAAIIIIAAAggggAACCCCAAAII+EHgyJEjpuVnamqqH47GIWwR2Llzp4wZM0YyMzNNkZo0aSKPPvqoRERE2FJEyuGjgFVjgDpPpKioKKlataqPVfnv5vqkdBKDDjsS3COAAAIIIIAAAggggAACCCCAgD8FkpOTZerUqZKWlubPw3KsEhZITEyUjz76SDIyMkxJGjZsKIMHD5Zy5cqVcMk4fVEErGoB2r59e1MX/eUkISGhUPVaunSpu98NN9zg5skggAACCCCAAAIIIIAAAggggAAC/hDQIfemTJlC8NMfmBYdQ4dT/OCDD9zrWq9ePRkyZIhERkZaVEqKUhgBqwKgHTt2dOswbtw4N1/QTE5OjsydO9dsXrduXdEnKgkBBBBAAAEEEEAAAQQQQAABBBDwl4C2/NTgZ3p6ur8OyXEsENizZ49X8FPjSk888YRoL2VS8AtYFQDVFpv9+vUzqqNHj5bPP/+8wMLZ2dkycOBA2bhxo9ln2LBhBd6XDRFAAAEEEEAAAQQQQAABBBBAAIGLCRw+fNh0e3e6R19se9YHh0BSUpJoHMoZy7VOnToydOhQqVChQnBUgFJeVMCqAKiW9tNPP5W2bduagv/xj3+UAQMGyPbt2y9Yka1bt0qfPn3kyy+/NNu9+uqr8uSTT15wH1YigAACCCCAAAIIIIAAAggggAACBRXQ2d4Z87OgWsGz3d69e72Cn5dffjnBz+C5fAUuacAnQXr55Zdl1apVFyygtubUdO7cOfnqq69k0qRJcs0110j9+vXNLSYmRnSmNb1t2LBB1qxZ4x6vVq1aZmDav//976KBUBICCCCAAAIIIIAAAggggAACCCBQFIHjx4+bbu9OC8GiHIt97RHYt2+fvP/++3L27FlTqMsuu8w0qKtYsaI9haQkfhEIeABUg58//fSTT4XPysqSuLg4c7vYjgcPHpS//vWvZjMCoBfTYj0CCCCAAAIIIIAAAggggAACCFxI4MSJEzJ58mQ3SHahbVkXPAL79+/3Cn7Wrl2b4GfwXD6fSxrwAKjPJWQHBBBAAAEEEEAAAQQQQAABBBBAoAQETp48aYKfKSkpJXB2TllcArt27ZIxY8a4QW3tTaxDKVaqVKm4TslxS1gg4AHQe++9V1q2bFnC1eb0CCCAAAIIIIAAAggggAACCCCAQP4Cp0+fNsHPM2fO5L8Ra4JOYP369fLFF19IZmamKbsGP//85z9LdHR00NWFAhdcIOAB0CFDhhS8dGyJAAIIIIAAAggggAACCCCAAAIIBFhAg57a7V2DoKTQEViyZIl89913kpOTYypVr1490TgVwc/Qucb51STgAdD8CsJyBBBAAAEEEEAAAQQQQAABBBBAoKQFtLu7Bj+1+zspdARmzJjhNSdNixYtZNCgQRIZGRk6laQm+QoQAM2XhhUIIIAAAggggAACCCCAAAIIIBBOAjob+JQpU0QnPiKFhkB2drZMnDjRa2Lt9u3bS9++faVMmTKhUUlqcVEBAqAXJWIDBBBAAAEEEEAAAQQQQAABBBAIdYHU1FTT8vPYsWOhXtWwqV96erp8+umnsnnzZrfO3bt3lzvuuMN9TCY8BAiAhsd1ppYIIIAAAggggAACCCCAAAIIIJCPQFpammn5SfAzH6AgXHzq1Ckz03tSUpIpfalSpeT++++XG264IQhrQ5GLKkAAtKiC7I8AAggggAACCCCAAAIIIIAAAkEroBMeTZs2TY4ePRq0daDg3gLJycnywQcfiBPQjoiIkIEDB0qrVq28N+RR2AgQAA2bS01FEUAAAQQQQAABBBBAAAEEEEDAU0ADZTo5jk58RAoNgV27dpmWnzqeq6aKFSvK4MGDpUGDBqFRQWpRKAECoIViYycEEEAAAQQQQAABBBBAAAEEEAhmgR07dsi8efMkMzMzmKtB2T0E1q9fL1988YV7TatVqyaPP/641KhRw2MrsuEoQAA0HK86dUYAAQQQQAABBBBAAAEEEEAgjAXWrl0rS5YskZycnDBWCK2qL168WL7//nv3mtatW1eGDBkilStXDq2KUptCCRAALRQbOyGAAAIIIIAAAggggAACCCCAQLAJaMDzl19+kYSEhGArOuW9gMD06dNNa15nkyuvvFIeeeQRiYyMdBZxH+YCBEDD/AlA9RFAAAEEEEAAAQQQQAABBBAIB4GMjAyZM2eO7N69OxyqGxZ1zMrKkokTJ0p8fLxb3/bt20vfvn2lTJky7jIyCBAA5TmAAAIIIIAAAggggAACCCCAAAIhLaAzvWsrwSNHjoR0PcOpcqdPn5Zx48aJTnrkpB49ekjPnj2dh9wj4ApYFQBNS0uTLl26yO233y4PPvig1KtXzy0oGQQQQAABBBBAAAEEEEAAAQQQQMBXAWZ691XM/u337t0rH3/8sRw/ftwUtnTp0nL//fdLx44d7S88JSwRAasCoDpT16+//mpu//M//yMbNmyQFi1alAgMJ0UAAQQQQAABBBBAAAEEEEAAgeAW2Llzp/z000/urODBXRtKrwI6gdX48ePda1qhQgUZNGiQNGvWDCAE8hWwKgCqAxE7qUaNGqKD1pIQQAABBBBAAAEEEEAAAQQQQAABXwWY6d1XMbu31wmsdAzX2bNnuwWtWbOmDB48WKpXr+4uI4NAXgJWBUAPHz7slrFTp05SqlQp9zEZBBBAAAEEEEAAAQQQQAABBBBA4GICGihbtGiRrF+//mKbsj5IBHQCqwkTJsi6devcEmuP4YcffliioqLcZWQQyE/AqgBo165dZf78+aasy5cvF33TIgia36VjOQIIIIAAAggggAACCCCAAAIIeApooGzu3LmSmJjouZh8EAucOHFCvv76a9m3b59bi9tuu03uuusu0bE/SQgURMCqZ0qfPn0kOjralFuf2F999VVB6sA2CCCAAAIIIIAAAggggAACCCAQ5gIpKSny448/EvwMoefB1q1bZfTo0W7ws2zZsmbS7F69ehH8DKHrHIiqWBUAbdCggRmcuE6dOqbuOhP8I488Ypo46xsZCQEEEEAAAQQQQAABBBBAAAEEEDhf4OjRo/Ltt9+K59B652/D4+AR0B7BOnnV2LFj5ezZs6bglStXlqeeekratWsXPBWhpNYIWNUFXmdn27JliwwbNkz+9a9/yf79++Wzzz4zNxWrXbu2NG7cWPRJX5A0Y8aMgmzGNggggAACCCCAAAIIIIAAAgggEKQCSUlJMmvWLNHu76TgF0hNTTXjfSYkJLiV0QZz2kAuJibGXUYGAV8ErAqALlu2TAYNGpRv+Q8cOCB6IyGAAAIIIIAAAggggAACCCCAAAKbNm2ShQsXyrlz58AIAQGN+YwbN86rJW/79u3l/vvvF+3+TkKgsAI8eworx34IIIAAAggggAACCCCAAAIIIFBiAtqIauXKlSV2fk7sX4FVq1bJxIkT3Za8ERERonPFtGzZUsqUKePfk3G0sBOwKgDarFkzefLJJ8PuIlBhBBBAAAEEEEAAAQQQQAABBBAomEB2drbMnz9fdIIcUvAL6PWcOnWq/Pzzz25lLr30Unn00UelevXqkpaW5i4ng0BhBawKgLZt21b0RkIAAQQQQAABBBBAAAEEEEAAAQTOF9Bg2MyZM82cIeev43HwCeh4nzr3i84H46SrrrrKzPQeFRUlmZmZzmLuESiSgFUB0CLVhJ0RQAABBBBAAAEEEEAAAQQQQCBkBU6ePCnTp0+X48ePh2wdw6liR48elTFjxsjBgwdNtUuVKiU9e/aUbt26ieZJCPhTgACoPzU5FgIIIIAAAggggAACCCCAAAII+F1Ag2QzZswQbTFICn6BxMREGTt2rJw5c8ZUply5cjJw4EDR1p8kBIpDIGgCoDomhL5AtFl0SkqKGQdCx4KoW7euxMTEFIcNx0QAAQQQQAABBBBAAAEEEEAAgRIW2L59u8ybN0+ysrJKuCSc3h8Ca9askfHjx7vXU2M6Q4YMkTp16vjj8BwDgTwFrA6AZmRkyJtvvilff/216Bteenp6rkqULVtWbr31VjMz2EMPPSSRkZG5tmEBAggggAACCCCAAAIIIIAAAggEn0B8fLwsX748+ApOifMU+Omnn0xLXmelBj01+EnDNkeE++ISsDYAGhsbK0888YT89ttvF6y7/gKkvwTpbfTo0fLNN99I8+bNL7gPKxFAAAEEEEAAAQQQQAABBBBAwF4BnfxmwYIFsm3bNnsLSckKLKC9eidOnChxcXHuPtrd/eGHH6YhmytCpjgFrAyA6iC4jz32mM/1XrdunbRr1868oK688kqf92cHBBBAAAEEEEAAAQQQQAABBBAoWQEdF1Jnek9OTi7ZgnB2vwjo9fz0009Nz17ngLfccov07t1bSpcu7SziHoFiFbAuALpz5075y1/+4lXpO++8Ux544AFp0KCBuUVHR0tSUpIZE3TTpk0yatQo2b17t9lHX1h9+vQxQdAKFSp4HYcHCCCAAAIIIIAAAggggAACCCBgr4BOdqTBz7Nnz9pbSEpWYAEdzvCLL76QkydPmn10dvff//73cuONNxb4GGyIgD8ErAqAnjt3zsz6pZMcaWrWrJmMGzdOOnXqlKuuTZs2Fb1169bNdJXX2cOeffZZM4juxo0b5aOPPpLnnnsu134sQAABBBBAAAEEEEAAAQQQQAAB+wR00mMdDk+7S5OCW0DjO3PnzpU5c+ZITk6OqUz58uVl0KBBQo/d4L62wVp6qwKgOhbE4sWLjWWlSpVk6tSpJgh6Mdxy5crJk08+KTppktN69OOPPyYAejE41iOAAAIIIIAAAggggAACCCBQwgIaIPv1119FZwcnBb+AtvbUVp/a+tNJOtmRBj+rV6/uLOIegYAKWBUA1TE8nTRy5MgCBT+d7fVeW3z++OOP5o1TfznavHkzvyx4ApFHAAEEEEAAAQQQQAABBBBAwCIBbcikrQSdYe0sKhpFKYSA9sidMGGCOD179RA333yz9OrVS8qWtSoEVYjasUswC1j17Fu/fr1r2aVLFzfvS+a2224zAVDdR8cTpWm1L3psiwACCCCAAAIIIIAAAggggEBgBE6cOGHG+zx27FhgTshZik1Ahy2YNm2aLFy40D2Hzsui87m0atXKXUYGgZISsCoAqhMaaYqMjJTmzZsXyqR169bufvv27XPzwZY5fPhwyI174oz7odciNTVV0tLSgu2ylHh5HUMdGJzkm4Bjp3tlZmYKhr756daO4ZEjR0QHLyf5JuD46V48/3yz060dP/2i5Ayi7/tRwncPx08F9DMGr2HfnwtqqK1ZmJSjcHbOXryGHQnf7p3XMP8/fHPTrR07zeuEuZ6t0nQZ6eICjqG/n3/79+83wTJtARqqybHT+mVlZZnnYCjWVQPYEydOFM8YTP369aVv374SExNTpHo7hvr6DafPL/r/0p+vOQ1Q+/N4RXkea2C8cuXKRTlEofa1KgDqjAWRnp4u+gW7Ro0aPldq79697j46xkSwpqioKK9/1sFaD89y6wtOA5+atOm7DoBM8k1APxzorWLFir7tyNbm9eR84C1durTomy7JNwH90KY/XKidGpJ8E9Dnn/MBjtewb3a6tdqpof5IGhER4fsBwnwP/f/rTCjBa7hwTwZ9/unnF30OknwT0M/2+uOjJv38RxdI3/x0a+cztH5HIPkm4Hx+0b30/wevYd/8dGt9DaujPz+/bNiwQZYtW2b+v4fy/3X9/OK8/+nn51B8/9OhDL///nu3gZMGKbVnbteuXf3ynUEnU9Lnn879Ek5J36v88ZrTwLEmvS7+OJ4/rkFJveatCoC2aNHCtVy5cqX07NnTfVzQTHx8vLtpMDez1kmgQi1p4M758KZP+Ojo6FCrYrHXR798qSN2vlPrP07101SmTBkMfSc0H36dAGhJ/dMqRLGt2cUzAMVr2PfL4ryG9cs/AQDf/fTLlxMA1Q+/ofgFzHcV3/bQlp/65YvXr29uztZOAEADoLyGHZWC3zt+PP8KbuZsqf9/nZ5nGlDA0JEp+L0G8fR/iD/s9Di//PKL6DiR4fB5Uj+/OK9fDYCGUgBe6zV58mRZsmSJ+2TSVn0PPfSQNG3a1F1W1IyexwmAhlMLUP1f6Y/XnBMA1eefP45X1OtZkvtbFQD1HK9z+PDh5hcDX94U9ZeH7777znhWqVJF6tatW5K2nBsBBBBAAAEEEEAAAQQQQAABBP5PQAMxs2fPtqYbLhel8ALJycny6aefig5j4CQdxvDBBx8M+yCb48G9fQJWBUBvv/120+1dX0wrVqyQZ599Vt59990CNZvWffr06eP+ute9e3f7tCkRAggggAACCCCAAAIIIIAAAmEmkJiYKPPmzXO/r4dZ9UOqutrrdtKkSaZnolZMWxbecccdohNZh1MLzZC6qGFSGasGcdPm0v/85z9d+tGjR5vZwrRZdX7p1KlT8tprr0njxo1l27ZtZrNq1arJO++8k98uLEcAAQQQQAABBBBAAAEEEEAAgWIW0C7gS5culenTpxP8LGbr4j68DsX29ddfy/jx493gp/a8feqpp0zvXYKfxX0FOH5RBaxqAaqVGTRokHzyySfmTVIf68zw9957rxkrSGcR01vVqlUlKSlJ9FckbXLtjGml22vSwGnNmjX/+4C/CCCAAAIIIIAAAggggAACCCAQUAHt8j5nzhw5cOBAQM/LyfwvoNfws88+8xq+4KqrrpIHHnjAmol1/F9rjhhqAtYFQPVXgwULFsgrr7wio0aNEv3FSJMOXr1582Zzy+8iaAtSHTu0b9+++W3CcgQQQAABBBBAAAEEEEAAAQQQKEYBurwXI26AD62THGmvXGcyJ51Q9u6775Zbb701wCXhdAgUTcC6AKhWR2eHfOutt6RXr14ycOBA2bVr1wVrqUFTHWz3jTfekFq1al1wW1YigAACCCCAAAIIIIAAAggggID/BbQB0/Lly2XVqlX+PzhHDKiAtuDVLu8bNmxwz6vDDWqM5oorrnCXkUEgWAQCHgB96aWXTJBSm0vfdtttF3S66aabzLieO3bscFt/aivQQ4cOSYMGDaRJkybm1rJlS2nYsOEFj8VKBBBAAAEEEEAAAQQQQAABBBAoHgG6vBePa0kcVeMuEyZMkNOnT7unb9OmjfTr188MT+guJINAEAkENACak5Mjb7/9tqSnp0t0dLToBEaeSceVWLdunVl0ww03mG20eXXTpk3NTVuEkhBAAAEEEEAAAQQQQAABBBBAwB4Burzbcy2KUhLt5j5t2jT55Zdf3MNERkbK73//e7n++uvdZWQQCEaBgAZAjxw5YoKfCpXXJEWxsbEyYMAA47h69WrRXxhICCCAAAIIIIAAAggggAACCCBgnwBd3u27JoUtkU4w/cUXX3hNWqWTUD/00ENy6aWXFvaw7IeANQIBDYAeP37crfiJEyfcPBkEEEAAAQQQQAABBBBAAAEEEAgeAbq8B8+1ulBJtafuokWLZOrUqZKVlWU21XlWunfvbm7aK5eEQCgIBDQAWqNGDddMW4POnz9funTp4i4jgwACCCCAAAIIIIAAAggggAACdgvQ5d3u61PQ0umwhF999ZWZc8XZ55JLLjGtPplnxRHhPlQEAhoArVKlitStW1eSkpKMX8+ePeXOO++URo0amYF0ExISXNcPP/ywyDO6Dx8+3D0eGQQQQAABBBBAAAEEEEAAAQQQKLwAXd4Lb2fbnuvXr5eJEydKSkqKW7S2bdtKnz59mOjIFSETSgIBDYAq3J///GcZNmyYMdQBdidPnpyn58cff5zncl8WEgD1RYttEUAAAQQQQAABBBBAAAEEEMhbQLu8z507V3SsSFLwCuik1D/++KMsW7bMrUT58uWlb9++cu2117rLyCAQagIBD4C+8MILouNJvPjii5KdnR1qntQHAQQQQAABBBBAAAEEEEAAgZAS2Lt3r6xZs0ZSU1NDql7hVpldu3bJ+PHjRYckdFLjxo3NZNTa9Z2EQCgLBDwAqpjPP/+8XHfddTJp0iTZs2eP6C9J2pQ+OTlZtmzZYryvueYaqVSpUijbUzcEEEAAAQQQQAABBBBAAAEErBXQCXLi4uJk5cqVUrFiRWvLScEuLKCNz7T1rt70mmrSyY3uuOMO6dy5s5QuXfrCB2AtAiEgUCIBUHW76aabzM3TUAffHTBggFk0btw4adOmjedq8ggggAACCCCAAAIIIIAAAgggEAABp8u7thp0gmYBOC2n8LPA4cOH5csvv5Tdu3e7R65Vq5aZ6KhOnTruMjIIhLpAiQVAQx2W+iGAAAIIIIAAAggggAACCCAQjALaU/Onn36iy3swXjyPMi9dutSM95mRkeEu1cZovXr1koiICHcZGQTCQcCqAOgVV1wh/fr1M+5Vq1YNB3/qiAACCCCAAAIIIIAAAggggIAVAtrSc8WKFRIfH29FeShE4QQOHTok3333nWzdutU9QOXKleWBBx6QK6+80l1GBoFwErAqANqpUyfRGwkBBBBAAAEEEEAAAQQQQAABBAInkJKSYsaI3LdvX+BOypn8KqAzvOs4n7GxsWaeFefgv/vd76R///6M4+qAcB+WAlYFQMPyClBpBBBAAAEEEEAAAQQQQAABBEpQICkpyXR5P3v2bAmWglMXRWDNmjUyefJkOXHihHsYnVi6d+/eZhJqdyEZBMJUgABomF54qo0AAggggAACCCCAAAIIIBDeAs4s79rlnYmOgvO5kFd391KlSskNN9wgd955p1SoUCE4K0apEfCzAAFQP4NyOAQQQAABBBBAAAEEEEAAAQRsF9Au7zrR0d69e20vKuXLQ8Dp7r5w4ULJzs52t6hfv7706dNH6tat6y4jgwACIgRAeRYggAACCCCAAAIIIIAAAgggEEYCdHkP7oudX3f3u+66S9q3by/aApSEAALeAgRAvT14hAACCCCAAAIIIIAAAggggEBICmRlZcny5ctFA2ik4BM4fvy4md19w4YNbuHp7u5SkEHgggIEQC/Iw0oEEEAAAQQQQAABBBBAAAEEgl8gOTnZdHnXIBopuAR0fNYlS5bItGnTRLu+O4nu7o4E9whcXIAA6MWN2AIBBBBAAAEEEEAAAQQQQACBoBQ4d+6crFy5UnSiI82TgktAJzn6+uuvZdeuXW7By5cvL7169ZKOHTvS3d1VIYPAhQUIgF7Yh7UIIIAAAggggAACCCCAAAIIBKWAtvacN2+eaBCNFFwCOrHR/PnzZe7cuaJDFzjpd7/7nZnkKCYmxlnEPQIIFECAAGgBkNgEAQQQQAABBBBAAAEEEEAAgWASWLt2rSxbtswreBZM5Q/nsiYmJsrEiRPlwIEDLkPlypXl97//vbRu3dpdRgYBBAouQAC04FZsiQACCCCAAAIIIIAAAggggIDVAqdPnzYtB/fu3Wt1OSlcbgEd33PGjBmyaNEi0XE/ndShQwfT5b1ChQrOIu4RQMBHgaAJgGrzb/0VZMuWLZKSkiLVq1c3t7p16wpNv3286myOAAIIIIAAAggggAACCCAQcgKbN282wbOMjIyQq1uoV2jTpk0yadIk8Zyk6tJLL5V+/fpJ06ZNQ7361A+BYhewOgCqb9pvvvmmGfB3+/btXrOdOTJly5aVW2+91YyB8dBDD0lkZKSzinsEEEAAAQQQQAABBBBAAAEEQl7g7NmzEhsb6zVRTshXOkQqqC12f/zxR1m1apVbo9KlS0vnzp2lR48eUq5cOXc5GQQQKLyAtQFQffN+4okn5Lfffrtg7XQwYB3UWW+jR4+Wb775Rpo3b37BfViJAAIIIIAAAggggAACCCCAQCgI7NixQxYuXCipqamhUJ2wqsOKFStk8uTJogFsJ9WpU0f69+8v2tuVhAAC/hOwMgA6ZswYeeyxx3yu5bp166Rdu3YSFxcnV155pc/7swMCCCCAAAIIIIAAAggggAACwSCg40XqWJE6TBwpuAQOHz5surtv3brVLXhERIT07NnT9HDVFqAkBBDwr4B1AdCdO3fKX/7yF69a3nnnnfLAAw9IgwYNzC06OlqSkpLMmKA6TsaoUaNk9+7dZp8zZ86Y7vAaBGWAYC9GHiCAAAIIIIAAAggggAACCISAgH4fnj9/vuj3X1LwCOjcJtrbdc6cOZKZmekWXHux9u3bV6pVq+YuI4MAAv4VsCoAeu7cORk4cKCZ5Eir2axZMxk3bpx06tQpV611EGC9devWzXSVHzt2rDz77LOiXeI3btwoH330kTz33HO59mMBAggggAACCCCAAAIIIIAAAsEooN3cly1bZr7zBmP5w7nMGrTWsT7379/vMlSsWFHuvfde05PVXUgGAQSKRcCqAKi22ly8eLGpaKVKlWTq1KkmCHqxmuugwE8++aTopElO69GPP/6YAOjF4FiPAAIIIIAAAggggAACCCBgvYA2Flq/fr3omJHM8G795fIqoF4vnbNEA9c5OTnuOh2+T4OfGgQlIYBA8QtYFQDVMTydNHLkyAIFP53t9V5bfOovKr/++qsZB2Xz5s2MBeoJRB4BBBBAAAEEEEAAAQQQQCCoBBITE2XJkiVy/PjxoCo3hRVZs2aNTJkyRU6ePOlyaDf3fv36+RzvcA9ABgEECiVgVQBUf9FyUpcuXZysT/e33XabCYDqTjqeKJMh+cTHxggggAACCCCAAAIIIIAAAhYIHDt2zAQ+nfkuLCgSRSigwL59++SHH36Q7du3u3voxEa33nqr3H777aK9WEkIIBBYAasCoDqhkabIyEjRQYALk1q3bu3upm86JAQQQAABBBBAAAEEEEAAAQSCRUBnd9fh4bSBkHZ9JwWPQEpKisycOdM0yvLs7l63bl3T3b1Ro0bBUxlKikCICVgVAK1evbrh1Tf8I0eOSI0aNXzm3rt3r7tPnTp13DwZBBBAAAEEEEAAAQQQQAABBGwV0ICZTui7fPly0cmOSMEjoIFqHYpPg59nz551Cx4dHW0mbm7Tpo1ERES4y8kggEDgBawKgLZo0cIVWLlypfTs2dN9XNBMfHy8u2mrVq3cPBkEEEAAAQQQQAABBBBAAAEEbBTQhjw6IbA2BCIFl8C2bdtMd3fP2d3LlCkjN998swl+ZmdnB1eFKC0CISpgVQDUc7zO4cOHS9euXX36lUQnUfruu+/MpapSpYpoM3MSAggggAACCCCAAAIIIIAAAjYKnDp1yozzuWPHDhuLR5kuIKCTUukERzrRkWfSuMZ9991nerRqy1DtFk9CAIGSF7AqAKqDAWu39+TkZFmxYoU8++yz8u6774oOFnyxpPv06dNH0tLSzKbdu3e/2C6sRwABBBBAAAEEEEAAAQQQQCDgApmZmaK9HteuXStZWVkBPz8nLLxARkaGxMbGyrx580Svo5MuvfRSE/hs2bKls4h7BBCwSMCqAGjlypXln//8p/zxj380RKNHj5aFCxfK3//+d7nnnnvyZNNfzN566y15++235fTp02abatWqyTvvvJPn9ixEAAEEEEAAAQQQQAABBBBAoKQEtmzZIsuWLZMzZ86UVBE4byEFVq9eLVOnThVt/ekkndFdG2DpDO9ly1oVYnGKyD0CCPyfgHWvzkGDBsknn3wiS5cuNRdIZ4a/9957JSoqSurXr29uVatWlaSkJElMTBQdZ+P8MTU0cFqzZk0uMAIIIIAAAggggAACCCCAAAJWCOh31yVLlsihQ4esKA+FKLiAxh9++OEH2blzp9dObdu2lV69eklMTIzXch4ggIB9AtYFQEuVKiULFiyQV155RUaNGiU6ZoYmnQVv8+bN5pYfo7Yg1bFD+/btm98mLEcAAQQQQAABBBBAAAEEEEAgYAK7d++WVatWyb59+wJ2Tk7kHwHtcTpjxgwzRF9OTo570Hr16pnu7g0aNHCXkUEAAbsFrAuAKlf58uVNt3b9JWXgwIGya9euCypq0PTBBx+UN954Q2rVqnXBbVmJAAIIIIAAAggggAACCCCAQHEKaLBMZwfXwCczuxendPEcW8dl/eWXX2Tu3LnuPCN6Jm10dffdd0u7du1E4xAkBBAIHgErA6AO30033WT+aeiMeE7rT73XLgP6S0uTJk3MTQcZbtiwobMb9wgggAACCCCAAAIIIIAAAggEXECHZ9PvrDpW5MmTJwN+fk5YdIH169eb2d09A9c6tqeO8dmtWzeJjIws+kk4AgIIBFzA6gCoapQpU0aaNm1qbtoilIQAAggggAACCCCAAAIIIICATQI6M3hCQoKsW7dOUlJSbCoaZSmggI7R+uOPP8rWrVu99mjdurUZ51MnWyYhgEDwClgfAA1eWkqOAAIIIIAAAggggAACCCAQygJnz56VtWvXmuCnBkFJwSdw4sQJmTNnjixbtkw8x/m8/PLLzYTM2vOUhAACwS9AADT4ryE1QAABBBBAAAEEEEAAAQQQCKCATo6zZs0a2bRpk+h4kaTgEzhz5ozMmzdPFi9e7HUNK1WqJHfeeae0b99eSpcuHXwVo8QIIJCnAAHQPFlYiAACCCCAAAIIIIAAAggggIC3wNGjR83ERtpN2rO1oPdWPLJZIDU1VRYuXGhu6enpblF1nM8bb7xRevToIVFRUe5yMgggEBoCAQ+APv/88xIXF+fq6axqzpuL/voyYsQId11RM4sWLSrqIdgfAQQQQAABBBBAAAEEEEAgzAV0fEid0T0xMTHMJYK3+jpEgbb21LiDDl3gJJ3N/frrrzeBz0suucRZzD0CCISYQMADoDowtL7pOElnyXNScnKy1zpnOfcIIIAAAggggAACCCCAAAIIBFpAA54a+NQAKCk4BTTmoON76jifOnSBZ2rTpo3ccccdUqNGDc/F5BFAIAQFAh4ADUFDqoQAAggggAACCCCAAAIIIBBCAocOHZIlS5YQ+Azia3ru3DkTvJ49e7YcOXLEqyYtWrQw43zWqVPHazkPEEAgdAUCHgDt37+/tG3b1hUtV66cm7/qqqvk5Zdfdh+TQQABBBBAAAEEEEAAAQQQQCBQAikpKbJ06VLZsmVLoE7JeYpBYP369TJz5kw5cOCA19EbNWokd911lzRs2NBrOQ8QQCD0BQIeAB04cGC+qldffbXojYQAAggggAACCCCAAAIIIIBAoAQyMzPNrO6rV68WzZOCU2DTpk0m8JmUlORVAW3pqTO7a8tPEgIIhKdAwAOggWLW2dwiIyMDdTrOgwACCCCAAAIIIIAAAgggEIQCv/32m2n1eebMmSAsPUVWga1bt5rA565du7xAdGxPHeOzdevWopMdkRBAIHwFrAqA6sDEY8aMMVdj+PDhUq9ePZ+vzIMPPijLly8XfeM7ffq0O8O8zwdiBwQQQAABBBBAAAEEEEAAgZAV0O7ROkGvjvdJCk6BHTt2mMDn9u3bvSpQrVo1M6t7u3btpHTp0l7reIAAAuEpYFUAdOfOnfLFF1+YK/H0008XKgC6cuVKcd78tNl706ZNw/PKUmsEEEAAAQQQQAABBBBAAIFcAtpQ5tdff5Vt27blWseC4BDYvXu3CXyeP1ZrlSpVpHv37tK+fXspU6ZMcFSGUiKAQEAErAqAFrXGOsvb/v373cPoTG8EQF0OMggggAACCCCAAAIIIIBA2Aro2J7a63Dt2rWSlZUVtg7BXPG9e/fKrFmzZMOGDV7VqFy5snTr1k06duwoZcuGVJjDq548QACBwgsE/J0hOztbpk6dKjk5OblKHRcX5y5bsGCBaIvQgiQ9ls7WN3nyZDl16pS7izZ7JyGAAAIIIIAAAggggAACCISvgH5f1F6CGzduJPAZpE8DHa5AA5/r1q3zqkGlSpWkS5cu0qlTJylXrpzXOh4ggAACngIBD4BqM/Tp06fL559/7lmOXPkXXngh1zJfFtSsWVMaN27syy5siwACCCCAAAIIIIAAAgggEEIC+/btE21cc/ToURMgY6Lc4Lq4GvicO3eurFmzxqsRVYUKFaRz585y8803M/lxcF1SSotAiQkEPACqNX3jjTdkypQpcuLEiWKpeEREhLz//vuM+VEsuhwUAQQQQAABBBBAAAEEELBbQL9rLl26VHSMyLS0NLsLS+lyCWjgWgOfOlyBZypfvrzceuutcssttzDhsScMeQQQuKhAiQRAa9SoIf/85z/lxRdf9Cqgjsly9uxZs0ybshd00GLdTn8Bqlixolx55ZUydOhQ0wze6+A8QAABBBBAAAEEEEAAAQQQCGmB9PR0iY+Pl/Xr14sOv0YKLgGdyHjOnDmSkJDgVXDt3q6tPW+77Tbz3d9rJQ8QQACBAgiUSABUy/XYY4+Zm2cZv/rqKxkwYIBZtGjRImnTpo3navIIIIAAAggggAACCCCAAAII5BLQcT41aKbzSqSmpuZazwK7BXRW99mzZ8umTZu8CqotPm+88UbT3V0bPJEQQACBwgqUWAC0sAVmPwQQQAABBBBAAAEEEEAAAQRUICMjw0xupC0+PSfERSc4BHTiY23xqUMVeKaoqCjT4lO7umtvTxICCCBQVAGrAqA6c9s333xj6tSgQYOi1o39EUAAAQQQQAABBBBAAAEEQlBAx/jUGcE1cKZBUFJwCWzbts0EPvXeM2mwU8f4vOmmmxjj0xOGPAIIFFnAqgDoFVdcIXojIYAAAggggAACCCCAAAIIIHC+gI4RqYHPXbt2nb+Kx0Eg8Ntvv5nA544dO7xKq3OAOIHPyMhIr3U8QAABBPwhYFUA1B8Vco6RkpJiJkVyHvvrXmcSnDdvnuzdu9fcypYtK/Xq1ZP69etL7969pUmTJv46FcdBAAEEEEAAAQQQQAABBMJeICsrSzRwpjOCHzt2LOw9gg3AGZ9Vv0frWJ+eKTo62kxspL1BdaIjEgIIIFBcAtYHQI8ePWpmhtcZ4vWN0zPpY73pP0Sd7e/MmTOyb98+mT59ukyZMsU89ty+KPn9+/fLf/7zH1m5cmWuw2j3Cx1zZsaMGXLvvffK4MGDaa6fS4kFCCCAAAIIIIAAAggggEDBBbRRi7b23Lhxo6SlpRV8R7a0QiA7O1tWr15tGhAdPHjQq0wxMTHSpUsX6dixo0RERHit4wECCCBQHAJWBkD1zfG9996TDz/8UI4fP14c9fbpmBpcfemll0QHaNZUpUoV6datm2n1qf+I9dfI+fPni77Bf//993L69Gl59dVXfToHGyOAAAIIIIAAAggggAACCIgcOnTItPbcvn27nDt3DpIgE9DGS8uXL5cFCxbkarF7ySWXmBaf7du3J/AZZNeV4iIQ7ALWBUC1W4MOeKxBRFvS6NGj3eDnddddJ6+99ppUrlzZq3j9+vWT5557zgRs586dKzfccIMZw8RrIx4ggAACCCCAAAIIIIAAAgjkEtBApwY8tcXn+a0Fc23MAisFUlNTZcmSJfLzzz/n+j5fq1Yt0+Lz2muvlTJlylhZfgqFAAKhLWBdAPSRRx7J9WbpyyXQMURuv/1200LTl/3y21a718+ePdus1mO/8soruYKfurJx48YybNgw01JUH8+cOZMAqEKQEEAAAQQQQAABBBBAAIF8BLRHnXZx1yHFdEgzUvAJaOMlDXouXrw411AFOslx165dpVWrVlKqVKngqxwlRgCBkBGwKgC6aNEiWbNmjYurrS2feeYZadSokZlw6L777jPrtHu55k+ePCk6C+CsWbNk4sSJZp1OSqRjderERP5I2r3dGW9GB2bWJvv5JR2/pHz58mZ73Y+EAAIIIIAAAggggAACCCCQW0AnM9LWnlu2bDFzOuTegiW2C+g11G7u2t1du717pqZNm5pGSXpPQgABBGwQsCoAGhsb65q0adNGli1bJqVLlzbLtKm8DpSsQU/9hXDEiBHutgMGDJAbb7xRhg4darqg9+/f3/z65OzrbliIjA683bZtWzly5Ig0a9bsgkfQ81WtWlUOHDggOjFSRkYGM9ldUIyVCCCAAAIIIIAAAgggEE4CiYmJZnxPbchCCk4B/b6rc2CsWrXKa4xWbeGpLT11vgx/NUgKTiFKjQACNgpYFQDdsGGDa/T666+7wU9dqOOEaAtM7Vo+b948M+GQ59ghjz/+uFSsWFEGDRokS5cuNZMR3X///e7xCpvRVqh6K0jSLhv6z0CTvuGXK1euILuxDQIIIIAAAggggAACCCAQkgI5OTlmUqNdu3aZMT61oQgpOAX0GmrgMyEhwasC2hBIGw1pV/eaNWt6reMBAgggYIuAVQFQzxnfr7/++lxGLVu2NAFQDTTqANnnt8h86KGHTFf4OXPmyFtvvSX+CIDmKsQFFkybNs1de+WVV7p5MggggAACCCCAAAIIIIBAuAhoTzht4akBM23xqZPjkIJXYPPmzaK9NfVaeqaIiAjp0KGDdO7c+YJDxXnuQx4BBBAoKQGrAqDOuCFRUVFSo0aNXCae44foINnnB0B1hz/84Q+iAVCdTV4nMNIxQQORtIv8l19+aU6lTf/vvvvuIp1WZ0EMtXR+nc5/HGr1LY76OGbOfXGcI1SP6WmmLRE8H4dqnf1dL8dM7528v88RysfT552T8HMkCn7vmPH8K7iZ55bnP/8cT89tyF9YQA35/3Fho/zWej7/MMxP6cLLHUObX7unTp0yATINeu7fv9/02LtwrUpmrWNZMmcPnrNmZ2eb+Tm0xafTy9EpvX5f1yHobr75ZqlUqZJZjKuj431/vsv5j7235lFeAo6Zc5/XNqG4TOvrz/d8fx+vqOb+GLLS1zIEJjpYwFLpTOo6c5wGLvXinD9LnGfAUwfM7tOnT64jN2nSxCzTXx23bt0qLVq0yLWNvxdoi9Tnn39edLxQTVqu3/3ud0U6zeHDh/36ZC9SYYphZ/0VmF+CCw976NChwu/MnuY9BsPCPxF0wHtS0QR4/hXeT79g641UeIGjR48Wfucw3/Ps2bOiN1LhBXQ8f72RCidg0/8P/b6m31m0pafePHvzFa52xb+XfkfUGyl/AfXRsT2XLFli5rXw3LJy5cpyww03SLt27SQyMtKs0u/CpIIJaJwDr4JZ5bWVE2/Ja10oLtPhQvz5nq/BVH8eryjmFSpUMHP8FOUYhdnXqgBo8+bNTR20JWhycnKu8UPObwGaV4WdX6B0nY5NUtwB0PT0dHnppZdkx44dpjh169aVwYMH51U0liGAAAIIIIAAAggggAACQSugwTFt3akBz71790paWlrQ1oWCewvojzs6m7tORHz+Dz2XXnqp3HTTTdK6dWszN4f3njxCAAF/CmjLSJ0APK9e0f48Tzgey8oAqF6IyZMny2OPPeZ1TfQJoL86acuP+Pj4PFuJ7t69292nuP8ha0T+xRdfNLPS60m1fG+++ab7a5hbkEJkdAIl7XYQSkl/cXDqpK17AzU8QSga6ng7JN8EtJWC/uqqieefb3bO1o6hvnbPb6HvbMN9/gLOMC+6Ba/h/J0utEYNdQLEkugyc6FyBcM6p3eNlpXXcOGumD7/9LnnOQln4Y4Ufnvp5z+nGx+v4cJdf+czTEl8ftbvXhrw3LNnjxw8eNC9llqTYHg96OcX5/mnn1/4H+L9HNTvtNoLMy4uTjw/q+hW2rjnlltuEaehUjBcb+/alfwjnn9FvwaOYag+/3Qy7+rVq7s3/cHBn98VPF/X/jxuUa5sSV1LqwKgnq01R44cacYUOX8yIf3VadGiReaf74wZM+Suu+7ycp8wYYL7uFGjRm7e3xn9xVO7ve/bt88cunbt2jJq1Ci57LLL/HKqqlWr+uU4Nh1EfzF2ut2VL19eqlSpYlPxgqIs2uxfP4TqmyLJNwHPJv/65QFD3/x0a23xrt3f9bVryz9P32tRcntozwbnRyCef75fB+c1HB0dLTr2GMk3AX3t6mtYk37GKIkgim8ltm9r7Tamzz39MZ7km8Dp06fdbp+8hn2zc7Z2hp+55JJLnEXFdq/BBh3zUcfy1Jtn13b9DB9sSb/8Ow1j9POL03U72Orh7/LqNV6wYIGsXLnSDRA759Dv4F26dBFneDn9/6GO2m2V5JuAfn5xum5r0IfPML756dbOa1jtgr0Rhr6HasO5mjVruvcaAC3O5Izhq8+/cP8OYlUAVMcA1RnkdIY5DTDqjHJ/+tOfZMiQIaLrNPXr188EQDWvs77/8MMPJlCqbyw68/ukSZN0lUnOG7bz2F/3GzduNC0/9dcyTTo26b///W9mvvMXMMdBAAEEEEAAAQQQQACBgAlogEtbeGrAU3vUOQHDgBWAEwVEQL8z64zu2uJz06ZNXufUlrFt2rQxgc/LL7/cax0PEEDAdwHt1avBTs+bdm0nlZyAVQFQZXjjjTfMjHL6T1cHSHe6lP/97383SgMHDpS//vWvorOuawDytttuM62RdEIdp2WDbti7d+9cY4iaAxTxj7Y+ff31193Bs9u3by/Dhw/nl5wiurI7AggggAACCCCAAAIIBE5Avz/t3LlTtm3bZnq1aXCMFJoC2gpbx/f89ddfTW8ez1pqq1j9TqsNkapVq+a5ijwCCPggoMHNOnXqmF7BGvQMRIt9H4rHpv8nYF0AtG3btvLtt9+alp/ODFWeXdm12bN2NX/44YfdroROS0znimqkXVtk+jtNnTrVtDLVbiGaNMj6zDPPBMXYN/624HgIIIAAAggggAACCCAQXALaFVeDnjqBq/a4c77XBFctKG1BBfQ662zua9eudb87O/tqd/Ybb7zRTG6kQ1OQEEDANwF93WjA07l5Tsjt25HYOlAC1gVAteI6rqd2wdDu7O+88454BkB1/QMPPGDGX+rbt6/oL5eeqX79+vLVV1+545V4ritKftasWaY1qnOMoUOHmu74zmPuEUAAAQQQQAABBBBAAAHbBDToqYGw7du3mxncCXradoX8Wx7tSanjemo3d2fsP88z6PflTp06me7ujOnuKUMegQsL6PjBOjxEvXr1zI3u7Bf2snGtlQFQhdJWnA8++KC55dUdQ4Ok+/fvN2/sv/zyixnMX8cs6dGjh/j7iai/kjotSnXQ3Zdfftmcx8YLSpkQQAABBBBAAAEEEEAgvAW0y7MGPDXwmVcQLLx1QrP2+t1YW3vGx8d7DQ2ntdXv1tdee60JfOrM7iQEELi4gI6LW+v/s3cf4FJUB//HD72XS5EqRUCaUtSABTQvatQkKBYsiBqSKBqJ7Un0je0fXyuKUWKMsaIJxJIoGJEgIokgYIJKVwQE6b13aX9/JznL3L27e2f2bpnd/Z7n2bttdnbmM3N3Z39zSuPGRv8zCj01cFGuD8JU+lrn9xShCkB1tkqjzZ133nk2+NROpqIdL1bRSMQKQqNHgo81bVke0+BKbuTeQYMGEX6WBZPXIoAAAggggAACCCCAQMoFNH6CCz1dV2IpfxNmGCqBAwcOmNmzZ9tKQaq0E10U2Ki2Z48ePRizIhqH+wjEECgqKrKBp0JPNW3XyQNK/giEKgB95ZVXbMfM6pz5nnvuMfPmzTOdOnXKqvb48ePNnDlz7DIo7ddoebfffruvZbrrrrtSXhvV1xszEQIIIIAAAggggAACCOS9wJYtWyLN2zds2JD368sK/kdg06ZNZtq0aWb69Olm586dxVhUeahLly62f8927doVe447CCBQXEDN2lXxztXypD/c4j75di9UAaiasruiUbM6duzo7mbtevTo0ZH3Vn85Gj3Pb/GOSu/3NUyHAAIIIIAAAggggAACCMQT2Lx5s63pqdqeCsIohSGgFonz58+3wecXX3xRYgArtY489dRT7aV27dqFgcJaIhBQQJXalDW1bNnSXmjWHhAwxycPVQDqPWupqvph6F/h66+/zvFNzOIjgAACCCCAAAIIIIBALgso9Fy6dKlR03bV+qQUjoBCbtX0VEWc7du3l1jxDh062Gbuxx13XNyu40q8iAcQKCCB6tWr21qeCj1V27Nq1aoFtPasqlcgVAHo2WefbSZOnGiXTx/wqnGZ7RD0vffe83pxGwEEEEAAAQQQQAABBBBIu8D69esjfXq6gYyqVauW9vflDbIvoNqe6g5OXcN9+eWXJWp7KtDp2bOnDT4bNmyY/QVmCRAIkYAbvMgFnqrxSUFAAqEKQPv3728eeOABo1ELV61aZUaNGmUGDhzIlkIAAQQQQAABBBBAAAEE8lpAlT9Uw1Mjt6t5e6zafnkNwMqZjRs3Rmp76jdxdGnbtq1t4t61a1dTqVKl6Ke5j0DBCtSsWTPSrF39eTJ4UcHuCglXPFQBaOvWrc2ECROMgtCVK1fakeAnTZpkbr75ZqMP+xo1aiRcGZ5EAAEEEEAAAQQQQAABBHJFYPfu3Wb58uX2smzZMrN3795cWXSWM0UCqu2pQXc1qJFqe0YX/QZWbU/170lNtmgd7heygP4flCG1auKFbBoAAEAASURBVNWK/41C3hECrHuoAtAlS5aYBQsW2FHWH3nkEbN69WozYsQIe9E6NWnSxAahfjt1Hjt2bAAKJkUAAQQQQAABBBBAAAEE0idw6NAhs3btWqOwU8GnmrlTClNA418o9PzXv/5VYiR3iWgE99NOO82O6F6xYqh+thfmBmOtsy6gWs+q3anQU83bVeuTgkAQgVB9kqpz50GDBsVdfvV94/q/iTsRTyCAAAIIIIAAAggggAACIRFQU2YXeK5YscJ88803IVkyFiPTAgcOHLC1PdW356JFi0q8vQIdV9uTvj1L8PBAAQqo3+M2bdqY+vXrm+7du9O0vQD3gVSucqgC0FSuGPNCAAEEEEAAAQQQQAABBDItoJBLLdkUeurCqO2Z3gLhez8F3zNmzLCXXbt2lVjA9u3b2ybuXbp0MRUqVCjxPA8gUEgCavF7zDHH2EvTpk1t1yBbt2411IQupL0gPesaqgBUH/xDhgxJz5oyVwQQQAABBBBAAAEEEEAgDQIKOdWkXYGnBnNVCEopbIFt27aZTz75xPz73/+O2YqxVq1a5uSTTzannHKKadCgQWFjsfYFK6DBioqKiky9evVsLU81cef/oWB3h7SveKgC0JNOOsnoQkEAAQQQQAABBBBAAAEEwiqgZuwatNWFnozYHtYtldnl0n4xd+5cG3pqbIvDhw8XW4By5coZVfpR357HHXcctT2L6XAnnwW07yvkVNcOLuzUtd/xXfLZhnXLnECoAtDMrTbvhAACCCCAAAIIIIAAAgj4F9i4cWOkL081cdeARhQEFHJqMF/V9Jw5c6Ztrhut0qhRI9OjRw9b2Ue13SgI5LtA1apVTePGje1A1tr/dVFtTwoC2RQgAM2mPu+NAAIIIIAAAggggAACoRTYu3evUd+NbgCjWH03hnLBWaiMCCgQV+ipvj03bdpU4j2rV69uTjzxRBt8asRqCgL5KlClShXbfF1N1xV0KvisW7duvq4u65XDAgSgObzxWHQEEEAAAQQQQAABBBBIjYBq8q1fv94Gngo9161bV6IJc2reibnkqsCePXtsLU8Fn6r1GV3Kly9vOnfubENPmrhH63A/1wU0CJGareuisFMjs+tSo0aNXF81lr9ABAhAC2RDs5oIIIAAAggggAACCCBQXGDz5s120CINXKTanqr1SUHAK6CuDtSfp0LPOXPmxBzgSgO39OzZ09b4JAzy6nE7lwXUjF2jsDdv3tw0a9bMhp3qy5OCQK4KEIDm6pZjuRFAAAEEEEAAAQQQQMC3gGp4qtmy+u/UAEZr1qwxqtFHQSCWgAJxjeKuy44dO0pMoia+GsBXfXuqyS8FgVwXUFN2BZ26KPRkNPZc36Isf7QAAWi0CPcRQAABBBBAAAEEEEAg5wVUc2/Dhg2RGp4KPjVKNwWBeALaXxR4fvrpp7Y7hOjpKlWqZLp27WpDz2OPPdaoyTsFgVwV0P6sGp6qwewCT2p45urWZLn9CBCA+lFiGgQQQAABBBBAAAEEEAi1wMGDB22/nWrOrsvatWvN/v37Q73MLFz2BbZt22Y+++wzG3ouX7485gK1bdvWNnHv1q2bUS05CgK5KKDAXoMUucBTNZcrVKiQi6vCMiOQlAABaFJsvAgBBBBAAAEEEEAAAQSyKaBwUyGnwk7V7tRthaAUBEoT2L17t5k9e7YNPRctWhRzsKsmTZrYJu4ayV2DvlAQyEUBDVKk2p0KPdW0vXLlyrm4GiwzAikRIABNCSMzQQABBBBAAAEEEEAAgXQKqPm6gk5dFHpqxHY1c6cg4EdAgbkGM1Lw+fnnn8cczEhBpwJP9e2pAJSCQK4JqG9aBZ66KPCsXr16rq0Cy4tA2gQIQNNGy4wRQAABBBBAAAEEEEAgWQGNyO5qdyr0VP+MGsiIgoBfAQXkX375pZkxY4aZP39+zD5ga9asabp3725Dz9atW/udNdMhEAoB7b+uhqeudZ+CAAKxBQhAY7vwKAIIIIAAAggggAACCGRQYNeuXcVqeG7atCmD785b5ZPA0qVLbfN29e25c+fOEqumfjy7dOliQ8/27dszmFEJIR4Iq0CNGjWKjdSuGp8UBBDwJ0AA6s+JqRBAAAEEEEAAAQQQQCCFAjt27ChWw3PLli0pnDuzKjSBNWvWREZw37x5c4nV12AvHTp0sCO4H3fccUYjYFMQCLuAmrCrKbsuquFZVFQU9kVm+RAIrQABaGg3DQuGAAIIIIAAAggggED+CGzfvt0OVLRx40YbfCoApSBQFgENfDVz5kwza9YsowA0upQrV860adPGHH/88aZz586mTp06jOIejcT9UAmohmfTpk0JPEO1VViYfBHIeACqJggHDhzIiB/VwTPCzJsggAACCCCAAAIIIFBMQGGn+uzUQEUrVqwwK1euNOrTs2rVqtS8KybFnaACpYWemp9GvNZARieccIIdBEb7HgWBsAloRPZGjRrZy1FHHWWv6cMzbFuJ5ckngYwHoBdffLGZMGFCRgzpJD0jzLwJAggggAACCCCAQAELqOm6wk53Uei5b9++iIhuawR3CgLJCvgJPRs3bmwHM9Io7gqTXNHo7xQEsi2gLhgaNmxojjnmmEjoSXP2bG8V3r/QBDIegBYaMOuLAAIIIIAAAggggEA+CKhygcJOBZyudqeasxNu5sPWDd86rFu3zmgQo3jN27XEqj2nEdx1adKkSfhWgiUqWAF1v6AgXs3Z1Y+nbuu6Xr16BWvCiiOQbYGcDEDVL4ZGiYxVypcvb3R2hYIAAggggAACCCCAAALJCRw8eNBoIBlvrU6FnZnqyiq5peZVuS6g0FN9euoSq09PrR+hZ65v5fxdftXoVPcLumjAIjVx12emPkcpCCCQfYGMB6BPP/20UZ9AicqLL75ofv/739tJKlasaK688kozYMAA06JFC/tBon4x1I/L8uXLzdKlS81zzz1nRo8ebXRWWmdWJk+ebNq1a5foLXgOAQQQQAABBBBAAAEEvhXQD/RNmzZFanbqx7ruKwSlIJBuAULPdAsz/3QJqGKWN/Ck/850STNfBFIjkPEAtG3btgmX/L333rOBpib67ne/a0aMGGFatWpV4jXqQP3YY4+1l3POOcc2jbjgggtsKHrWWWeZKVOm2MC0xAt5AAEEEEAAAQQQQACBAhVQc3WFmwo5FTzpWs3aDx06VKAirHY2BAg9s6HOe5ZFQOGmKlupH88GDRrY2wSeZRHltQhkXiDjAWiiVdTBWP/+/e1ZaAWlb731lvHbMXC3bt3s9L169bIh6NVXX23++c9/Jno7nkMAAQQQQAABBBBAIG8FNPiQAk7vRWEnA4Xm7SYP9YqtXr3azJkzx1fzdv22U9+JFASyIVC3bl0bdCrsVOipwLNatWrZWBTeEwEEUigQqgBUtTZ37NhhV+/RRx/1HX46D434p+byakI/ffp0O/pklSpV3NNcI4AAAggggAACCCCQlwJ79uyxQacboEih57Zt2/JyXVmp3BDQmA1ffvmlWbBggfniiy/i7o+uT09Cz9zYrvmylOpqr3bt2kZhpypduev69evbvjvzZT1ZDwQQOCIQqgD0o48+iiyZanImU0455RQbgKp5zyeffGJOO+20ZGbDaxBAAAEEEEAAAQQQCKWAgiUFnC7s1PXOnTtDuawsVOEIqBuFr7/+OhJ4aryGeLWNVavuhBNOMISehbN/ZGNNNRK7CzkVcHovtWrVMnqeggAChSMQqgD0008/tfIaLc1v0/foTaW+QV3RmUYCUKfBNQIIIIAAAggggECuCCg4UqipWpzu4vruVABKQSAMAupSQb+5VMtTtT1VEzlWUdCkAW07depkunbtSvP2WEg8lrRApUqVbH6gDEGXevXq2es6deqYChUqJD1fXogAAvklEKoAVGcCVVR7c/bs2UZN2oOWqVOnRl6iEdkoCCCAAAIIIIAAAgiEUUCjrG/fvt1eFHJu3bo1clv3GYU9jFutsJdJv9MWL14cqeWpwYziFdW269ChQ+RSvXr1eJPyOAK+BLQPeQNOF3iqNicFAQQQKE0gVAGod4T4559/PnAAumTJEvPaa6/ZddZZRp1hpCCAAAIIIIAAAgggkC2B/fv32xqcCjoVcLranLpW3/fxmghna3l5XwSiBdasWROp5anw88CBA9GT2PvqU1G/5xR6duzY0TRp0iTmdDyIgB8BhZsahMhdVFmK8T38yDENAgjEEwhVADpgwADz8MMP2wPBZ5991vYLc91118Vb9mKPb9682VxwwQVGzTBUfvCDHxhqgBYj4g4CCCCAAAIIIIBAGgT27t1bLNj01uSkuXoawJllWgX8Dl6khdAARgo7FXoq/FRXZhQEggio4pLCTgWcuijw1Kjr7EtBFJkWAQT8CIQqAO3cubMNLseOHWuXffDgweaNN94wd955p+nTp0/M9dEB5hNPPGGefPJJ22RIE6mfj7vuuivm9DyIAAIIIIAAAggggEBQgd27d9sanKtWrYrU5HS1Ovft2xd0dkyPQGgENHjRsmXLbC1P9eeZaPCiatWqmfbt20dqeSq4oiDgV0Bhp0ZZd0GnrnVffXhSEEAAgXQLhCoA1cq+8MIL5tRTTzVqzq7ywQcf2Iv6+2jevLm9qDNjHXzqi1qjXkY3Hfrd735nTj75ZPt6/iCAAAIIIIAAAgggUJqAQiA1SXehprepum7rpLt+pNMEszRJns8FATVrX7hwob2oWXtpgxe5Wp6tWrUy5cuXz4VVZBlDIFCjRg3TuHFjG3jqWjWGCTtDsGFYBAQKVCB0Aag+FCdMmGD69etn5s2bF9ksOuvuvqQjD0bdUEg6dOhQc/3110c9w10EEEAAAQQQQACBQhdQ34UKOL3hpmuurscVglIQyEeBjRs3Rn5L6TfVzp07466mKpu4wFO1PRViURAoTUB9wKrpuoJOd2FwotLUeB4BBDIpELoAVCvfpk0bM2vWLPPMM8+YYcOG2ZqeiVCqVq1q+vbta8PP1q1bJ5qU5xBAAAEEEEAAAQTyWECjVLtQ09XcdIFnotAnj0lYtQIU0D7vKo/o2o2TEItCNfL0+4vBi2Lp8Fg8AQXlLuhUJSb13Unt4HhaPI4AAmEQCGUAKhj14zlkyBB7mT9/vq0V6pq86+BVH7AaWbBLly7m+9//vqlZs2YYPFkGBBBAAAEEEEAAgTQKqJamWgapuboLNl3QqVqc8ZrypnGRmDUCWRfQwEWLFi2KhJ7qJixeUUjVsmVLc+yxx9qLmrXTLDmeFo87AdXmVM1g1y0dNYOdDNcIIJArAqENQL2AGhxJFwoCCCCAAAIIIIBA/gqo9qaCHJ3sjnet8DO6//f8FWHNEIgtsHfvXvPVV1/ZwFPB58qVK2NP+O2jGnimWbNmkcBTtT3pyzYuF0/8V0AVjBR2at9RbU81cVdNTwoCCCCQqwKhCkD1RX7WWWeZ8847z1x11VWmRYsWuerKciOAAAIIIIAAAgj8V0CBpYJLBZvR4aaCThd2KgClIIBASYH9+/fb0dm//vprG3pqpPZEfdYqqHI1PNu1a2c0VgIFgUQC1apVs2Gnq+FZVFQUmZza9REKbiCAQA4LhCoAfeWVV8zUqVPt5Z577rGDIHXq1CmHeVl0BBBAAAEEEEAgvwUUzLgA011Hh5wKPxOFNfktxNohEFzg4MGDNvB0/XguXbrUaBCveKVevXqRwFPBZ+3ateNNyuMIWIHKlSsXCzw1gBEFAQQQyGeBUAWgH374YcT6qKOOsn2MRB7gBgIIIIAAAggggEBGBRRculDTXUeHm/v27cvoMvFmCOSjgE4QqBn74sWLbQ1PNW9P9L+l/hhdDU9d169fPx9ZWKcUCqhJe9OmTe04GhpLQ4GnukegIIAAAoUiEKoAdMOGDRH3Xr168YEc0eAGAggggAACCCCQOgHVLosOMr33dVvhp6ajIIBA6gXU3YMGeF2yZInty1PXibqAUPPkY445xlYQUZN2BVgUBOIJKNhUwKn9RBcFnwwaHE+LxxFAoFAEQhWAnn322WbixInW/uOPP7Yd3HNWqlB2RdYTAQQQQAABBFIhoD7VvWGmam6uWbPGqA833daxVaKgJRXLwDwQQKC4gE4oqBm7anbqoj48E51gUPNkDVbkanmqibtGb1cQSkEgWkD7i/p9dWGnbusxCgIIIIDAEYFQBaD9+/c3DzzwgNmxY4dZtWqVGTVqlBk4cOCRpeUWAggggAACCCBQoAJqIuuaoSe6jtVP4J49eyL9B9aoUcMGKQXKyGojkBGBrVu3RsJOBZ46CZGoaFT21q1b29BTNTxbtmxpKlSoEHmJ/ocpCDgB1eZ0YWfjxo1Nw4YNaT3pcLhGAAEE4giEKgDVl/6ECROMglD1gaOR4CdNmmRuvvlm07ZtW6MDdgoCCCCAAAIIIJBvAqqR6a216b3twk7VIKMggEA4BdatW1cs8Ny8eXPCBVWApSbtquWpi0beVg1PCgLRAhUrVjQaH0NBp7vwuzhaifsIIIBA6QKhCkDV982CBQvM7bffbh555BGzevVqM2LECHvRqugsl4JQv6Majh07tnQBpkAAAQQQQAABBFIscPjwYdvMXM3RdVHAqWvV4ooVbmokdQoCCOSGgBuwyDVn128Y/V8nKhqkyIWdulagRUEglkDdunVt0Klm7Ao81Zcn4XgsKR5DAAEEggmEKgCdPn26GTRoUNw1UNOR0pqPxH0xTyCAAAIIIIAAAgEFFExqJGZdXJjpve8e9z6m6XSfggAC+SGgExhBBixSP7uquOENPOvUqZMfGKxFSgXU9YELOt111apVU/oezAwBBBBA4D8CoQpA2SgIIIAAAggggECqBVRbywWVLpyMFVh6p1Hgodqaei0FAQQKS0DdTbjR2VXLc8WKFQkHLFJfnS1atIgEnurWq3r16oWFxtqWKqBgXDWBXTN2XRcVFZX6OiZAAAEEEEiNQKgC0Pbt25shQ4akZs2YCwIIIIAAAgjkjYBrUq6RzDVYYnSA6ZqYewNOV2OT5uV5sxuwIgikRWDLli3FAs/SWpx5ByxSP56tWrUylSpVSsuyMdPcFVAwrq4OmjVrZmsEq1aw9h0KAggggEB2BEIVgJ500klGFwoCCCCAAAII5KeARihXMBkvsHS1MKOvXd+ZahpI0JCf+wZrhUAmBFSrWwGnani6iwLQREUDFnmbsyvQok/GRGKF+VzlypVt0OkCTzVpVwhKQQABBBAIh0CoAtBwkLAUCCCAAAIIIJBIQAGCCzCjg0rvfXfbBZ4KMQ8ePJho1jyHAAIIpFRAnz+u/86lS5caXfTZlKgwYFEiHZ5zAgrGVauzadOm9qL9Rs3cKQgggAAC4RQgAA3ndmGpEEAAAQQQSLuAQkwXUura3XaBpa51ifVc2heON0AAAQSSEHDN2VW7U2HnqlWrjLrQiFcUWCnAUlN2V8uTAYviaRX24/Xq1SsWeNauXbuwQVh7BBBAIMcE8jYA3bVrl6lRo0aObQ4WFwEEEEAAgWACqlFZWmDpDTBdyKlrBvgJZs3UCCAQLgF9hingVNDpmrNv3bo14UKqD8aWLVvawNP138mo2wnJCvJJdXHQsGFDG467Wp7VqlUrSAtWGgEEEMgXgdAHoJs2bTIaiVEDGESfvdV9XdSfmH7I7dy50x4EvfPOO2bMmDH2fr5sKNYDAQQQQCB/BfRd5pqUR9e49AaW7rZ3Ggb4yd/9gjVDAIHiAupGwzVnV+D59ddf28/O4lMVv1e3bt1I2KnR2em/s7gP9/4joP47NSq7agMr8NTtihVD/1OZzYcAAgggEEAglJ/qa9euNU899ZR55plnTGmdkgdYVyZFAAEEEECgzAI66eYuqn2pi/e+bsd6bOPGjfaEnsJL1SKJDjzLvGDMAAEEEMgzgc2bN0dqdirw1OBF0RUivKus5uwKOFWzUxcFnkVFRd5JuI2AFVD/nS7s1DX9d7JjIIAAAvkvELoAdNasWeb00083O3bsyH991hABBBBAICkBNXt0QWOssFGPqWakrt3z7tq9znvtbsd7nUJLPVeWoq5ZXJPzWrVqlWVWvBYBBBDIOwF9xkY3Z9+2bVvC9VTT9VatWtmg0zVnVxN3CgJeATVnV/+dGpVd+0u7du2MAlAKAggggEBhCYQuAP3xj39cpvBTPyrPO+88873vfa+wtiRriwACCGRBQD9YdVGA6C7usbIGkLFCSQWRiWr/ZIGAt0QAAQQQSEJAzdnVhN313amm7aoZn6ioNqer3alrNVVWuEVBwCtQqVIlG3a6vjs1qJW6VFNR8En46dXiNgIIIFA4AqEKQCdPnmxmzpwZ0e/Ro4e55ZZb7IiMK1euNBdffLF97u6777a3dVZ4xYoVZty4cebVV1+1z6mvlscee8y0aNEiMh9uIIAAAvku4MLHWKGhCyjdtZvGe60fna5PZe/j3jBT/U+qdr5q12gagsh836tYPwQQQCB1AmrOvnz5clvLU8Fnac3ZFWyqObuasSvs1AjtjM6euu2RT3PScYmasWt/0bUGL/IG4wrbXQCaT+vNuiCAAAIIBBMIVQA6adKkyNJ3797dTJ8+PfLldeKJJ9qDHoWe8+fPN/fff39k2oEDB5revXubG2+80fYZesUVV5gpU6ZEXhuZkBsIIIBAGgWim2XHqgEZ6zGFid7Q0XvbG0C62256XZdWWyaVq6v3048IBZ8VKlRI5ayZFwIIIIBAngjou2rdunVGlRe8F9XgT1TUnN2FnbrWSO00Z08kVrjPVa9evVjg2aBBg8LFYM0RQAABBHwLhCoAnTdvXmTB77vvvmIBpn5s9+rVy7z77rvm/ffft00uvT/Ab7jhBlOjRg0zaNAgM23aNPPXv/7VXHrppZH5cQMBBApHwAWFLmxUcKfb69evt58dGiRBJ1P0uKbVdbzbseYV/RrNW6+nIIAAAgggUEgCOgm3evXqYkGn7vv5TlSfjN7m7Bp121trr5AcWdfEAmqy7mp36pqBrRJ78SwCCCCAQGyBUAWg3hHfe/bsWWKJO3fubAPQnTt3msWLF5v27dsXm+bqq6+2TeHHjx9vHn/8cQLQYjrcQSDzAt4AMjpIdCGiu04UQOo57/PRr3H39UPMDTITvbaqtajPDhWdPFHtAQoCCCCAAAII+BNQE2JvjU7dVk1PP92hVK5c2SjgbN68uWnbtq0dhIbm7P7cC3GqunXrFqvhWbt27UJkYJ0RQAABBFIsEKoAVGGJSrVq1cxRRx1VYlWPPfbYyGNz5swpEYDqyQEDBhgFoBpNXoGJ+gSlIFDIAsk0y04UNnqfc7ddAKn7uuh/2c8PokLeLqw7AggggAACYRVQpYTosNNbUSHRcqu2noJO70UBljvOV1N3DVJDQcAJqDawt4anWvVREEAAAQQQSLVAqNJBnRFW350KUBSeqJmqt3hrfM6ePdv079/f+7S93a5dO3utmmALFy40nTp1KjENDyCQDQGFhLq4WpEuNIz1mAsWvde6vWvXLjsIjX5I6L73eTc/95juZ7J/yGyY8p4IIIAAAgggkLyATpJu2LChRNip4w0/RcGVN+jUbdXeiy4aRI+CgAT0+06DFHkHLVIoTkEAAQQQQCDdAqEKQDt06GDXVwGR+upr1KhRsfWPrgFa7Mn/3tFZZ1fmzp1LAOowuLYCCta9AaG7HR1Kuse9197b0WGj9znd1kU/Ktx8dT8VRYGmfkTUqlUrFbNjHggggAACCCBQIAI6FtHI696anatWrfJ1slShlY7Lo8NOupMpkJ2nDKupln3ad9QFgi66rS4RKAgggAACCGRaIJQBqBBGjx5trr/++mIeahavmm/bt283M2bMiFlLdNmyZZHXlDbaZGRCbmRcQOGgwjwFibq4oNAbLOpA3T2v2+6+99p72/ta9zo3XzedHqcggAACCCCAAAL5LKBjYIWbCjtXrFhhr9euXRu3n2yvhZqnN2nSxIadRx99tL1WbT2arXuVuB1LQINYqXanQk4XesaqERzrtTyGAAIIIIBAugVCFYB6m6s/+OCD5owzzjAdO3YsZtCtWzczefJko4O4sWPHmr59+xZ7fuTIkZH7bdq0idzmRngE/va3v9kQm+Yu4dkmLAkCCCCAAAII5KaAKgZ4a3Xq9saNG32tjGrnRdfqVHDFaOy++Ap+IrVIcrU6da3wk/EXCn63AAABBBAIrUCoAlD1AdqnTx8zadIkeyB3yimnmGuvvdYMHjzYjhgpxcsvv9wGoLqtUd/ffPNNG5SqRqFGfn/99df1lC2uP1B3n+twCDA4Tji2A0uBAAIIIIAAArkloGAzOuxUAOqnaNT16LCzfv36fl7KNAjYGsCuZqer3clgRewYCCCAAAK5JBCqAFRwQ4cONb179zZqurNt2zYzbNgwU6VKFfPAAw9Y1x/96Efm3nvvtWe2t27das4880zb2fqePXts34gOv1+/frbphbvPNQIIIIAAAggggAACYRfQiWId46q1k/ei/jv9dO+k/jobNGhQIuyk//Cwb/lwLV9RUVGk307V7lRYHj1AbbiWmKVBAAEEEEAgsUC5bw+yDieeJPPPvvPOO7bm57p16+ybv/TSS2bQoEGRBRk1apS55pprbP+QkQc9N9Sx9rx580wu1wDdtGmTr36aPKsd+pva1dQH59tvv222bNnCQVQSW0yGutA0LQm8b1+imuKuYOgk/F+z//m3ijUl+18slWCPyVA/wPkRHsxNU7v/X93m808KwUuq9z/NT8dDGvjTe9Hxr/ow91MqVKhg1Ee++uh0F/XfqcoDYSre/Y//4eS2jAxV0vH5p/1F+5EuasauSz4NVKT/NV2cn/5vKMEE9BtO+yBdHARz09Ryc+NQ6P+X/S+4ofsfll06PgODL1FuvULjobgSlv9hdYeYjROzoasBqg2jfj2XL19um7MPHz7cRPfleeWVV9rBkC677DKjmp/e0qpVK6OANJfDT62POpp3X9Te9cvl2/rg58O/bFtQ+4QM+eIM7qiDD/c/xcFHcD+9wu1/Ck84+AhuqH1QFxX+h5Pz0z6o/Y8AL7if9+CX/+HgfnqF9r9kvj/0va2m6wo5FW56r91xkZ8l0o8F1cRT0NmsWTN7UVPkXPg80Xq6zz/+h/1s7ZLTuP/hVGxv9f2qfUkXBeb53hWC9j8NvqoiPwb0Krl/+XlE+yB2fqSKT+OOn/WovkMwLO7j5572PTnKjt8gfsSKT+O+P8K0/6Xiu6z4Wvq7F8oaoNGL7n7wRD+u5kFTpkwxH374oT0b1b17d3Puueca9XFECZ+ADjxUs1U1QHfs2GEYBCn4NpLhvn37snK2JPjShusV+uG1c+dOu1D6wK1evXq4FjAHlkZfnjrpJLtsfWnlAFPcRdy1a1ckhM/GGc+4C5YjT7j/YX138OMh+EbT/647AFa/fYTIwQ31HaJ9L17tStXaVLipZusKOl3zdT2mY1m/RZ+xCqZcP4suqFKT5FwtOnZxART/w8ltRVfpQ+Fl0KL/eRea6zqX96Wg667pZaffjSo1a9bkONpKBPujvoblqM8lSjABffdu2LDBvkjfH/Xq1Qs2A6aO/A9r/+P4JfgOoS50VPT7TTX9C7mEsgZo9AaJt5PXrVvX1haNHgk++vXcRwABBBBAAAEEEEAgFQIK87wBpws8VcvT1XL08z5uBG0XcOqHnWrjcYLEjx7TJBKoXbu2rSmsQa9UY5jKIYm0eA4BBBBAoFAEQhWAKpmePXu2rc2p0eDjBZ+JNs73v/998+WXX9pJvvrqq0ST8hwCCCCAAAIIIIAAAjEFdu/eXSzoXLVqla3h6WqSxXxRjAd1wt6FnN5rWiLEwOKhpARUo1M1O133CKrlSEEAAQQQQACB4gKhCkAnTZpkBg4caJdQo7irL8+gB4crVqwwS5YsKb6W3EMAAQQQQAABBBBAIIaAuuWJrtGpWp1q8um3qF8tNWv0Bpzudrwm837nzXQIRAuoz07XpF2hZ9DfS9Hz4z4CCCCAAAKFIBCqANQLPmbMGHP66acbjQiv5kAUBBBAAAEEEEAAAQSSFdi2bVukX07XP6eu1T+v36LWSQ0aNCgRdKr5On3T+lVkuiACCtfVZ5vrMqFjx470ox8EkGkRQAABBBD4r0BoA1At36effmp69uxpQ9CuXbuy0RBAAAEEEEAAAQQQiCugPjg3b94cs0bn3r17474u+omKFSuahg0bFgs61a+iTspT2y5ai/upFFDIrkDdNWnXPle5cmW7X+t9GEQ0ldrMCwEEEECgkARCHYBqQ6hJe69evczrr79u1L8nBQEEEEAAAQQQQKCwBQ4ePGg2bdoUqdHpbcKu0dj9FgVLCpvUXN1d67ZqeUb3Ra9R4DWCKgWBVAooUHfdJSjsVG1PBfAUBBBAAAEEEEitQGi/XYcNG2YefPBBs2XLFqMDzvPPP9888cQT5uc//3lqBZgbAggggAACCCCAQKgEFHBqsCHV5nQXBZ7uto4Pg4y4rlpzLmRy1wo81W+nmhhTEMiEgPY1hesKOrX/6ZoR2jMhz3sggAACCCBgTGgD0B49ephp06bZWp9Lly41OhC+6aabzKJFi2wQyhl4dl8EEEAAAQQQQCA3BQ4cOBAJOBVsKtD0BpwKP4MEnE6hRo0aJYJOBZ6ETE6I60wKuOBdQaf2Q4We9BWbyS3AeyGAAAIIIHBEILQB6L59+0yHDh3Mxx9/bPr27Wv+/e9/26V+6qmnzFdffWWbxNesWfPImnALAQQQQAABBBBAIBQCaoauENMbarram7rWgETJBJxaOTVNr1u3bszBiDg2DMXmL8iFUO1O1Sh2YacCz6KiooK0YKURQAABBBAIo0CoA1CBqR+cf/zjH2bAgAHm7bfftobjxo0zp512mhk7dqw5+uijw+jKMiGAAAIIIIAAAnkroIAzutamCzgVem7fvj3pdVcrHwWcCpPq169vr3XbXfRcdP+cSb8ZL0QgSYEqVapEmrG7Ju3qU5aCAAIIIIAAAuEUCG0AeujQoYiYOgd/6623zC233GJUA1Rlzpw5kRHiTzzxxMi0NCuJUHADAQQQQAABBBBISuCbb75JGHDu2LEjqfnqRRrgJVHAqebqBJxJ8/LCNAmoNqer3alrBfIUBBBAAAEEEMgdgdAGoNGEOhD+7W9/a1q3bm1+8YtfGAWka9asMaeffroZOXKkufDCC+1LFJZSEEAAAQQQQAABBOILqKshV2PTe+2arGsAymSLTkYr4IxVe1OP1a5dm4GHksXldRkRUE1O9depZuwu9FSNTwoCCCCAAAII5K5AzgSgjvjWW281LVu2NAMHDjR79uwxu3fvNpdccon5zW9+Y26++WZDAOqkuEYAAQQQQACBQhXYu3dvsYBz/fr1tj9O9cupy65du5KmUcCp2nCJAs6kZ84LEciwgCpZaH9u2LBhJOzUvq0+PSkIIIAAAgggkD8COReAiv6iiy4ykyZNMueff77ZsGGDrQ2q5vGqEVqtWrX82TqsCQIIIIAAAgggEENAJ4G9NTd129Xe1G2dIE62qPab629T1wqDvIFnrVq1kp01r0MgqwLqX1b7s8JOXTTWgO6rWwYKAggggAACCOS3QM5+25988slm+vTp5rzzzjOLFi2yW2no0KGGzsfze4dl7RBAAAEEECgEAQWYLuBUsBk94JAC0GSLmvImCjgZST1ZWV4XJgH9JnBhp4JOBZ7a7+lfNkxbiWVBAAEEEEAgcwI5G4CKqE2bNjYEveCCC8zUqVOtmjrtpyCAAAIIIIAAAmEWUBN0b8DpbrtrNWFPtlStWrVEwFmjRg3b96Zr6ksIlKwurwujgEJ9F3I2aNDAhp3a12nGHsatxTIhgAACCCCQHYGcDkBFpjO7EydONNdcc4154403sqPIuyKAAAIIIIAAAt8KHDx40GiE9G3bttnL9u3bI7f1mPrfVMipQYiSLerux9Xg1HGQ97ZCn1j9oavG6IEDB5J9S16HQGgEFOa7kNOFnhpYi4IAAggggAACCCQSCFUA2qlTJ/PLX/7SLm/btm0TLXex51TT4bXXXrODIz322GPFnuMOAggggAACCCBQVgEFmxoZPV6w6YJOhZ9lLQowvaGmu+2u6e+8rMK8PlcE1N+sCzldv50KQCkIIIAAAggggEBQgVAFoN27dze6JFPUxOXRRx+1AySl4sdHMsvAaxBAAAEEEEAgtwQOHTrkO9g8fPhwSlZOfWx6BxVywaauVaNTzXkpCBSagOueQbU7XeipSg4UBBBAAAEEEEAgFQKhCkBTsUIaHImCAAIIIIAAAoUt4IJNVzMzXs1NnTRNVbCpQVfq1KljL2qS627r2t2vW7cuAzYW9q5Z8GvvHYndBZ0KPRnItOB3DQAQQAABBBBIq0DeBaBOS4ML0ETGaXCNAAIIIIBAfggorFRo6Q02vbcVdLr7qQo2K1WqFAkzXZDpwk13X8EmNTfzYx9jLVIn4EZi1zG5um5QDedWrVoZNW2nIIAAAggggAACmRQIfQC6adMms3v3brN///4SNTT0w0YXdeqvwQTUN9eqVavMO++8Y8aMGWPvZxKT90IAAQQQQACB5AT0fa6Tly68jFVjUwMIKfxU7c5UFAWbLsCMDjTdfV3TDDcV2swjnwXUFZX+VxRwqjanu9ZjKvq/1XG6SsWKof/5YZeTPwgggAACCCCQXwKhPAJZu3ateeqpp8wzzzxjtmzZkl/irA0CCCCAAAIFJqBgM1ag6a2tqdupDDZVw8yFmPFCzlijpRfYpmF1EQgsoJrOLuD0XuuEAgUBBBBAAAEEEAirQOgC0FmzZpnTTz/dnikOKxrLhQACCCCAAALGttCIDjFj3dcI6qkoqjnmDTYVcMYKN+kCJxXazKPQBVSrU107eENOjcRO8/VC3zNYfwQQQAABBHJTIHQB6I9//OMyhZ86KDvvvPPM9773vdzcIiw1AggggAACWRbYs2ePrxqb6oImFUWDorgg011H197U42omr+bo1DRLhTrzQOCIgP6vFHTqopDT3aa5+hEjbiGAAAIIIIBAbguEKgCdPHmymTlzZkS0R48e5pZbbjFt2rQxK1euNBdffLF97u6777a3VctkxYoVZty4cebVV1+1z+lA7bHHHjMtWrSIzIcbCCCAAAIIIGCMgk1vH5ve296am+p3OxWlfPnyNtiMF2q6Gpx+apQp/HR9CKZi2ZgHAoUo4Gp1qp9OF3Tqds2aNQuRg3VGAAEEEEAAgQISCFUAOmnSpAh99+7dzfTp041+PKmceOKJti8v/UCbP3++uf/++yPTDhw40PTu3dvceOONts/QK664wkyZMiXy2siE3EAAAQQQQCAPBTQQoL4fvSFmrPvffPNNStZe382uKXqicFOhigIXCgIIZF7AjbqugNMNTFSvXj0GIcr8puAdEUAAAQQQQCAEAqEKQOfNmxchue+++4oFmGoe16tXL/Puu++a999/36g/MT3myg033GDU59egQYPMtGnTzF//+ldz6aWXuqe5RgABBBBAIOcEFGzGq6XpAk5dpyrYVFjpgk1XO9M1RffeV7DpTlDmHCoLjECeCeh/saioKBJyurCTWp15tqFZHQQQQAABBBAok0CoAlDviO89e/YssWKdO3e2AaiawC1evNi0b9++2DRXX321bQo/fvx48/jjjxOAFtPhDgIIIIBAWAQUWJZWW1PB5969e1OyyAo2FYZ4Q0wXbHprcCr8JNhMCTkzQSAtAtWrV48MSuSCTtXq9FYKSMsbM1MEEEAAAQQQQCDHBUIVgLo+x9Rk56ijjipBe+yxx0YemzNnTokAVE8OGDDAKADVaPIanIHO2yNk3EAAAQQQSKGAWiKoT013UVip27revXu3vfbe37Vrl63NuWPHjpQGm2r94MLMeAGnQk6CzRRufGaFQAYE9P+skFPHxLpWn536f6cggAACCCCAAAIIBBcIVQDatm1b23engksNdhDdb5i3xufs2bNN//79S6xxu3bt7GOqXbNw4ULTqVOnEtPwAAIIIIBAYQscOnSoREDpDTBdqOkCzOj7aomgeaSzqMamt3am97YLOvUYNb/SuRWYNwLpF9DJCdXiVMCpiws7K1eunP435x0QQAABBBBAAIECEQhVANqhQwfLrpqg69evN40aNSq2GaJrgBZ78r93vP0dzZ07lwA0FhKPIYAAAjksoOBRfWMGCSxdgKnXKLzUibZsFbVyqFu3bqTWZqxgUwEnwWa2thDvi0D6BBRqKuB0IacCT5qwp8+bOSOAAAIIIIAAAk4glAGoFm706NHm+uuvd8tpr9UESD8U1S/ajBkzYtYSXbZsWeQ1+qFLQQABBBAIj4Bq9+sklwsko2tYulDTPR99X83IUzXgTzIqapmgAEMhZtWqVe119O1497Xu6r9PXbOor00KAgjkt4Caq7uw0zVj18kPCgIIIIAAAggggEDmBUIVgHqbqz/44IPmjDPOMB07diym0q1bNzN58mSzdu1aM3bsWNO3b99iz48cOTJyv02bNpHb3EAAAQQQKLuAak6qD0uFkNHhpZ/76hszm6VSpUolwst4gaV7XKGlCzt1O9mi8DbdzeaTXTZehwACZRNQsKmw0wWduk1/nWUz5dUIIIAAAggggEAqBUIVgKoP0D59+phJkyaZlStXmlNOOcVce+21ZvDgwUbPqVx++eU2ANVtjfr+5ptv2qBUPyo18vvrr7+up2xx/YG6+1wjgAAChSygQXuia1QGua/wMpsBnpqEK5R0waS79gaUCiqj77vXKIyI7lu6kPcH1h0BBIIL6HPI9dXp+utU2El/ncEteQUCCCCAAAIIIJBJgVAFoFrxoUOHmt69e9sf6du2bTPDhg0zVapUMQ888IB1+dGPfmTuvfdes3HjRrN161Zz5pln2r7UVPNIfcK50q9fvxJ9iLrnuEYAAQRyTUDhpbffy3hNxL2Bpve2wsts9nup4DFeYOkCSvd8rPsKL+kTM9f2WpYXgdwWcP11usBTa9OkSRN73Jnba8bSI4AAAggggAAChScQugD0pJNOMm+88Yat+blu3Tq7RbxN2fXD+MknnzTXXHONUSCgoiDUW3TA+uijj3of4jYCCCCQNQH1/ajwMrqJuDegjH7Oe1/T6ZKtovBSJ6JUu1LXCiNdSOmahse6757T9Gp6TkEAAQTCKqDPKVej0wWeGozMW3RcqhHbKQgggAACCCCAAAK5JxC6AFSE6tdz+fLltjn78OHDjTcA1fNXXnmlHQzpsssus4GCHnOlVatWZtSoUYbm706EawQQKKtA0P4uFVaqxqU34CzrMpTl9W7QHoWULpSMvl3afb2/apAqmFUTc2pjlmWL8FoEEMiWgE7ouP46vX12lqV/32ytC++LAAIIIIAAAggg4F8glAGoFl8/2K+66ip7idXnnELS1atXmylTppgPP/zQjqrbvXt3c+6555roM/b+OZgSAQTyTcCFdgokN2/ebGtiKtD09ocZHVh6g0u9TjU4s1U0Ynis2pWlBZbuef2op9/LbG093hcBBLIpoBM19evXt4MTuVqduq9jTAoCCCCAAAIIIIBAYQmENgD1boZ4zY10Bl9BaPRI8N7XchsBBHJXwIWUiQJKb1Nx3fbeV3jpusrIhoI+u1x46QJJv/dVU1NNMuN9/mVjfXhPBBBAIKwCCjVdyOmasterV4/P0LBuMJYLAQQQQAABBBDIsEBOBKAZNuHtEEAgBQKquR2r30tvQBkdWEY/p5qa2SoKHtXfZbzA0tuUPFa4qZqXqr1JQQABBBBInYA+m3UCvKioyCjgdKFn7dq1U/cmzAkBBBBAAAEEEEAg7wT4dZ53m5QVQqDsAmry7fq9jA4lvc3Do5/z3td02SreQXtcgKnAUoGkrhVO1qpVK2a46Z5X+ElBAAEEEMiOgGp0KuR0F4WdCj51oWZ8drYJ74oAAggggAACCOSyAAFoLm89lh2BOAL79++3TcG9gaRuB7mfzX4v9cM3Xg1LF1C652PdV+gZ3e+l1mfnzp1WTP3CMeBFnJ2HhxFAAIEMCqirD1eb04Wduq5Zs2YGl4K3QgABBBBAAAEEEMh3gYwHoP369TMTJ07MiKsLOzLyZrwJAikSUJ+VLqyMDiy1T+ui5uVuGm+NTNdXZqyBw1K0eKXOxg3akyigVPjofd7V0nTX1O4plZkJEEAAgZwR0AkpDVCpWpzekFP3GZAoZzYjC4oAAggggAACCOS0QMYDUIU2u3btymk0Fh6BeAIKHr2BZHSAmeg5F2hq1PJsFQWP8WpUunDSBZex7ivYVO1KCgIIIIBA4QlUqlSpWMDpanYq/OS7ofD2B9YYAQQQQAABBBAIk0DGA9AwrTzLgoBXQE2kFVB6Q0rv7egw03tf02nE8WwO2qMaNgonEwWUCi29z3tv6zlq4nj3CG4jgAACCMQS0Mmu6Jqcuq++lSkIIIAAAggggAACCIRRIOMBaN++fU3btm19WYwZM8asXr3aTnvGGWeYzp07+3odExWmgEYcjxdYuoDSPR/rvh7LZr+XbsRxbyjpva2AUrVr1MRcPzRjhZmFueVZawQQQACBVAvopJpGVndBp2u+rmsGiUu1NvNDAAEEEEAAAQQQSLdAxgPQIUOG+F6nzz//PBKAXnHFFWbw4MG+X8uEuSXgBu1JFFCqhqX3eddk3NXEzGa/lwomowPJIPcVdPrp91I1TBX0Ussmt/ZvlhYBBBAIq4BOqmlkdRdwusBTj+k5CgIIIIAAAggggAAC+SDAkW0+bMUcW4cXX3zRrF271gZ5LrzMZr+X6pdMYaWfwNI7jbd2Jn2b5dhOyOIigAACBSagWpsNGjSwgxG1bNnS3lboqRNqqu1JQQABBBBAAAEEEEAgnwUIQPN564Z03VatWmXWrFmTkqVzg/bECjC9AaU3uIyeVrU3KQgggAACCOSDgLfZumpzupqdOuGoFgQqDRs2pHZnPmxs1gEBBBBAAAEEEEDAtwABqG8qJkyVgAJIFdU4cf1eJgooowNLBZsagEHX9EOWqq3CfBBAAAEEckVArQ7URN01V3chp+7Ha7auAJSCAAIIIIAAAggggEChChCAFuqWz+J633DDDUY/xOrUqUOzuyxuB94aAQQQQCDcAjrJFyvk5Psz3NuNpUMAAQQQQAABBBAInwABaPi2Sd4vUc2aNe1o6/Q5lvebmhVEAAEEEPAhoO9Fby1O13RdrR0oCCCAAAIIIIAAAgggUHYBAtCyGzIHBBBAAAEEEEAgoYC6bVH/nLq45usKPXW7cuXKCV/LkwgggAACCCCAAAIIIFA2AQLQsvnxagQQQAABBBAocAG1aFBtTYWbGlVd16rV6b0fr2/OAqdj9RFAAAEEEEAAAQQQyIgAAWhGmHkTBBBAAAEEEMhVAQ06pEBT4aYLON1td12+fPlcXT2WGwEEEEAAAQQQQACBvBcgAM37TcwKIoAAAggggEAiATVBdwFnrNqbNWrUSPRynkMAAQQQQAABBBBAAIGQCxCAhnwDsXgIIIAAAgggUDYB9b8ZXXPTG3TqeQoCCCCAAAIIIIAAAgjkrwABaP5uW9YMAQQQQACBvBdQ/5uqoentc9M1S3d9cFaqVCnvHVhBBBBAAAEEEEAAAQQQiC9AABrfhmcQQAABBBBAIMsC6n/TBZwHDx40jRs3Ng0bNiwWeNL/ZpY3Em+PAAIIIIAAAggggEDIBTIegI4YMcIsWrTIF8uSJUsi040ZM8YsW7Ysct/PjYceesjPZEyDAAIIIIAAAlkSUO1Mb+1Nb9N01eRU+KlanocOHTLr1q0zdevWNdWqVcvS0vK2CCCAAAIIIIAAAgggkIsCGQ9AX3vtNTNhwoTAVuPHjze6BCkEoEG0mBYBBBBAAIHUC1SpUsW4pujR/XDqPmFm6s2ZIwIIIIAAAggggAACCBQXyHgAWvztuYcAAggggAACuSygGprePje9tTn1uEZYpyCAAAIIIIAAAggggAAC2RQgAM2mPu+NAAIIIIBAiAXUt6YCTRdw6tpbm1P31UcnBQEEEEAAAQQQQAABBBAIs0DGA1A1Yz98+HCYTVg2BBBAAAEECkJA/W8qxIyutemCTtf/ZkFgsJIIIIAAAggggAACCCCQtwIZD0A1kIEuFAQQQAABBBBIr4D633Rhpgs5vYMM0f9mev2ZOwIIIIAAAggggAACCIRDIOMBaDhWm6VAAAEEEEAg9wVUQ9MFmwo63cU1U6f/zdzfxqwBAggggAACCCCAAAIIlF2AALTshswBAQQQQACBlAuo/00FnC7MjA43dZ/+N1POzgwRQAABBBBAAAEEEEAgDwUIQPNwo7JKCCCAAALhF6hYsWKkxqbCTG/TdN1XzU66jAn/dmQJEUAAAQQQQAABBBBAIPwCBKBl3EZz5841N954o/0R++6775ZxbrwcAQQQQCBXBVQbs2rVqkb9buo6+vbu3buNmqSrVmebNm1M9erVc3VVWW4EEEAAAQQQQAABBBBAIKcECEDLsLl27NhhHn74YUa1L4MhL0UAAQTCJqCR0eOFmIke1+sSlfXr15uDBw/aSQg/E0nxHAIIIIAAAggggAACCCCQWgEC0CQ9d+3aZW677TazYsWKJOfAyxBAAAEE0img2paxamImCjE1Pf1qpnOrMG8EEEAAAQQQQAABBBBAIPMCBKBJmM+bN8888sgjZtmyZUm8mpcggAACCPgVUB+YLrB019FNy+M9Tv+ZfpWZDgEEEEAAAQQQQAABBBDIbwEC0ADbd8+ePea5554zb775Js3eA7gxKQIIIKARzaNrY8YLLqMfRw8BBBBAAAEEEEAAAQQQQACBsggQgPrUmz9/vrnvvvvMmjVr7CtUs2jgwIFGAx9t3rzZ51yYDAEEEMhtAY1crtHJ1YdldKDprZkZHWKqOToFAQQQQAABBBBAAAEEEEAAgWwIEID6VJ82bVok/Kxfv7659957zQknnGDGjx9v50BTS5+QTIYAAqEQUCAZHVImCjT1nMr27dtNgwYNTGkD/oRiJVkIBBBAAAEEEEAAAQQQQAABBL4VIAANsBvUrl3bXHbZZebiiy82NWrUCPBKJkUAAQTSIxA0xHTTq0l60LJv376gL2F6BBBAAAEEEEAAAQQQQAABBLIuQADqcxP06dPHXHnllbbZp8+XMBkCCCDgS0BhpAsm3bW3OXm825qW2ue+iJkIAQQQQAABBBBAAAEEEECggAUIQH1u/DZt2vickskQQKBQBSpUqGD7xQwSYircpH/MQt1jWG8EEEAAAQQQQAABBBBAAIFMCBCAZkI5ifdo1aqVWb16ta9Xqj/Sn/zkJ76mXbBggTnnnHN8TauJ3nnnHdOlSxdf0//xj38099xzT6nT1qpVy9Z2GzJkSKnTugnGjRtnFi5c6O4mvG7ZsqW58MILE07jffKFF14wO3fu9D4U9/Ypp5xievbsGfd57xPqK/Gll17yPpTw9iWXXGKaN2+ecBr35L///W/z3nvvubulXt98882+awpOnDjRzJs3r9R5aoImTZrYbiF8TfztRK+88orZsmWLr8lPOukk06tXL1/T7tmzxzz77LO+ptVEF1xwgWndunXc6dW/pesjU/vdiBEjzOHDh82hQ4fstW57L+5xzXDu3LlG3WWUVg4ePGjuuOMO8+KLL5Y2qX2+Q4cOgbZ53759zZw5c3zN++qrrzb333+/r2n37t0bqAuQp556ypx//vm+5j1lyhQ7uJyvib+d6OOPP7b7oJ/pH3/8cfPb3/7Wz6Tm6KOPNh999JGvaTXR5ZdfbqZPn+5r+osuusg88cQTkT6lS3uRTr4dOHCgtMns84888oi54oorfE372WefBfqc/OCDD0zbtm19zVv/iw899JCvaevVq2dmzpzpa1pNpO86fUb5Kfque+655/x5rTPBAABAAElEQVRMaqc5/vjjbR+3fl5w9913m2uvvdbPpObLL7803/ve93xNq4mCfO+OHDnS3HXXXb7mrRMz7nt0w4YNpb7mpptuMm+//Xap02kCfVaPGjXK17Sa6OSTT/b9P3DbbbcZfYf5KStXrjSnnXaan0ntNK+99prR97qf8tZbb5lbb73Vz6R2mq+++spo0Dg/5Ve/+pX585//7GdS2//86NGjfU2ric4880yzePFiX9MPHjzY3Hnnnb6m1Xd5t27dfE2riXSsdfbZZ9vpt27danSJV3R8c91118V7usTj+q6rU6dOicdjPfDAAw+Y559/PtZTJR5r3769mTBhQonH4z2gY4tZs2bFe7rY4xpI9cEHHyz2WLw733zzjWnXrl28p0s8Pnz4cNOvX78Sj8d6QN91auHmt+i7rmnTpr4m13fdk08+6WvaZs2aGY234LcMGDDATJ061dfk+k3gXQ4d8yc67g/yvavvOr9+2je0j/gt+q7zu931Xed3f6pbt66ZPXu238UwP/3pT83777/va3p91/n9/9IMu3btmvCzwPum+q7z+7mwaNEic9ZZZ3lfnvC2vuv8fp4F+d7V74noz191J+UGVY5eKH3XjRkzJvrhmPf1Xef3e0MzOPXUU82qVatiziv6QX3X3XLLLdEPx7yveWrefouW2e/3tCz8fv/r/bXd/VYq0Xed3+MW7Rt+j4e0HPquc8dbup+oaJ/2exyn7039z/gt+l+MPv7U789Y+5/+x/W/7rfo86yoqMjX5PpsinU8rt+fOjbIdPF3ZJbppeL9zP79++3FD4V2ZL9FQY3mnY7id95a3iDLrGV14ZOf5dZyBClaFr+v0XL4LUGWWfNM57z9LrOmk4VfD7/TufdP97zd+7hrvZ9c3bZwtxVw9ejRIxJyKhhwF31xqianKwrfE/1Qc9Mlc619z+//o9/p3HIE+QzxG7B55+1ul3Yd5H9d2yvIegb5nwlirR+bQUo6rXWg7Pf/LIi17MJgHWQZtE20r/p9TRAPzTvIdvS7TTTfoNZB5h1kvw7aXUYQ62Q+Q9K5HeXupwSxDrod/by/mybIdvTr5uYdZL8Osh2DegSx1rRB1lPL4rcE2a+Dfhdoer/LHcRa6+Z3vpo2iHXQ7RjEOhf3a2ftd/sEsU7nfp1O6yD/M37d5KwS5PMpiHU69+ug8/7Pmvr7m87tGMQ6yHYM6hFkOwbx8Cd8ZKog+3WQz1+9Q7qs3byPrEXiW0Gsg34+JX7n4s/G2456PBuFADQb6j7eU2dCvSFMopfoTJ7fadXcVuGP36Lp/c5btd5Km7c+JFUjQmfEggzCUr16dV+16rReGqAqyLy13H7XUR5+5615+qkJ6LaFHxP56aKQLsi8tcx+f/wGsa5Zs6ZvD62nav/6/bCrVq2anbeW29XGdCFl9LVqJaoGsop84hX3XMeOHX3XbNY6lrZfe99P29Hv/qTab37nrdq2fuer5dH0fmvb1q9f39e85aft4XeZtRza5n6XW/+7Qeat/cDvvHWW0u+8VbvF73y1jo0bN/Y1b/nJWsXv/Fu0aOH7fybIZ5n+v/x6aHmDfBeoJpbfeQf5/pJfw4YNfc9b0/p11jqqBv6OHTt0s9SidfQ772x/77qV8daK8POdEMS6UaNGvj20PDrG8Vs7Msg+onX0u+9pOfR/4Hc76rvAtdLw832q+fqdtz4X/C63Pm/8ztdZ+x3ATt9Jfucd1FrHFq6Utv+F5Xs36HeBvnc3b97sVjPhdYMGDXxba5to/3DHMKXtf0G+d7Vd/O57WqEg37v63/U776DHOPrM8TtvfZZpn3NhgPx0P17RfP0GQLn4vRvk+0tGRx11lO/9T9P6/QzRvPVdoP3VTwmy3NpP/e4feu8g3wVaXr/zdr8J9L/r9j+9XzwjfS74nXfQ7wJ9niXa77VcruiYOd4yumncdVBrHev7nXe0tRwTff7puMLvvINYB/180vRqneinBPne1f7kd//Qe3utvb+/YxkF/Q3m9m2/6xhrubXu2Sjlvt2R4qcF2ViiHHtPNWdUUzJ9KI8dOzbHlj6zi6sz45s2bbJVyPUjUz8KKcEEZKgfMn4PFoLNPb1T60tX21wXfVl6r0u7nYol04HHunXr7Kz0oa0vPkowAe17+nEnOxlSggmsX78+Embq4IgSTMD9D+tHtX6sUIIJ6H/XBWEKBPwGkMHeJb+n1neI9r0gJyDzW8T/2um4zzU75n/Yv5t3SheuZutHo3dZcu22wgjXmkcBey4eR2fbXF17yVHhMyWYgAJ11/WMfgPxPxzMT1O7/2Htf36D3ODvkr+vcM3eFX7qREUhF2qAFvLWZ90RSEJAP5pLCyxjPe+tfZTE2/ISBBBAAAEEEEAAAQQQQAABBBBAICkBAtCk2HgRArkvoEDS1cSMFVjqsViPU2so97c9a4AAAggggAACCCCAAAIIIIBAIQkQgBbS1mZd81IgmRBTwSbNB/Jyd2ClEEAAAQQQQAABBBBAAAEEEEAgSoAANAqEuwhkQ0AdOsercel9XH3gaWQ5DYziamcm6gw6G+vCeyKAAAIIIIAAAggggAACCCCAAAJhEiAADdPWYFlyXkAdC7tg0htclnbbb/+Yu3btMuqEXAMIUBBAAAEEEEAAAQQQQAABBBBAAAEEShcgAC3diCkKUEAjXCfTtJyRsQtwZ2GVEUAAAQQQQAABBBBAAAEEEEAg1AIEoKHePCxcWQVUs7K02pexgk7V5KQggAACCCCAAAIIIIAAAggggAACCOS+AAFo7m/DvF8D9XHpQkp37aeZuaZloJ+83z1YQQQQQAABBBBAAAEEEEAAAQQQQCChAAFoQh6eTIdArVq1TPXq1U29evUiwWaiQFO1OBnoJx1bgnkigAACCCCAAAIIIIAAAggggAAC+S9AAFrGbfzWW2+VcQ6F9/I+ffqYatWqMZBP4W161hgBBBBAAAEEEEAAAQQQQAABBBDIuED5jL8jb4gAAggggAACCCCAAAIIIIAAAggggAACCGRIgAA0Q9C8DQIIIIAAAggggAACCCCAAAIIIIAAAghkXoAANPPmvCMCCCCAAAIIIIAAAggggAACCCCAAAIIZEiAADRD0LwNAggggAACCCCAAAIIIIAAAggggAACCGRegAA08+a8IwIIIIAAAggggAACCCCAAAIIIIAAAghkSIAANEPQvA0CCCCAAAIIIIAAAggggAACCCCAAAIIZF6AADTz5rwjAggggAACCCCAAAIIIIAAAggggAACCGRIgAA0Q9C8DQIIIIAAAggggAACCCCAAAIIIIAAAghkXoAANPPmvCMCCCCAAAIIIIAAAggggAACCCCAAAIIZEiAADRD0LwNAggggAACCCCAAAIIIIAAAggggAACCGRegAA08+a8IwIIIIAAAggggAACCCCAAAIIIIAAAghkSIAANEPQvA0CCCCAAAIIIIAAAggggAACCCCAAAIIZF6AADTz5rwjAggggAACCCCAAAIIIIAAAggggAACCGRIgAA0Q9C8DQIIIIAAAggggAACCCCAAAIIIIAAAghkXoAANPPmvCMCCCCAAAIIIIAAAggggAACCCCAAAIIZEiAADRD0LwNAggggAACCCCAAAIIIIAAAggggAACCGRegAA08+a8IwIIIIAAAggggAACCCCAAAIIIIAAAghkSIAANEPQvA0CCCCAAAIIIIAAAggggAACCCCAAAIIZF6AADTz5rwjAggggAACCCCAAAIIIIAAAggggAACCGRIgAA0Q9C8DQIIIIAAAggggAACCCCAAAIIIIAAAghkXoAANPPmvCMCCCCAAAIIIIAAAggggAACCCCAAAIIZEiAADRD0LwNAggggAACCCCAAAIIIIAAAggggAACCGRegAA08+a8IwIIIIAAAggggAACCCCAAAIIIIAAAghkSIAANEPQvA0CCCCAAAIIIIAAAggggAACCCCAAAIIZF6AADTz5rwjAggggAACCCCAAAIIIIAAAggggAACCGRIgAA0Q9C8DQIIIIAAAggggAACCCCAAAIIIIAAAghkXqDc4W9L5t+WdyxUAe/uVq5cuUJlKNN6yxC75AjZ/5Jz876K/c+rEew2+18wr1hTs//FUvH3GPufP6dEUzlDvoMTKcV+ztnpWfxiG5X2qDPErzSp2M87Pz2LYWyjRI86P+wSKcV/zvlpCgzjOyV6RobYJRKK/xz73xEbAtAjFtxCAAEEEEAAAQQQQAABBBBAAAEEEEAAgTwToAl8nm1QVgcBBBBAAAEEEEAAAQQQQAABBBBAAAEEjggQgB6x4BYCCCCAAAIIIIAAAggggAACCCCAAAII5JkAAWiebVBWBwEEEEAAAQQQQAABBBBAAAEEEEAAAQSOCBCAHrHgFgIIIIAAAggggAACCCCAAAIIIIAAAgjkmQABaJ5tUFYHAQQQQAABBBBAAAEEEEAAAQQQQAABBI4IEIAeseAWAggggAACCCCAAAIIIIAAAggggAACCOSZAAFonm1QVgcBBBBAAAEEEEAAAQQQQAABBBBAAAEEjggQgB6x4BYCCCCAAAIIIIAAAggggAACCCCAAAII5JkAAWiebVBWBwEEEEAAAQQQQAABBBBAAAEEEEAAAQSOCBCAHrHgFgIIIIAAAggggAACCCCAAAIIIIAAAgjkmQABaJ5tUFYHAQQQQAABBBBAAAEEEEAAAQQQQAABBI4IEIAeseAWAggggAACCCCAAAIIIIAAAggggAACCOSZQMU8Wx9WJwcE5s6da2688UZTq1Yt8+677+bAErOIuSwwbdo08/7775uVK1faS8WKFU2LFi1Mq1atTL9+/Uy7du1yefVY9hwU2LVrl/0M3Lx5s+ndu7f55S9/mYNrwSLngsC+ffvMG2+8YWbPnm2WLVtmtm3bZpo3b25atmxpfvjDH5oTTzwxF1aDZcwRgWSP77788kszZcoUs2DBAqPbKtpHu3TpYgYMGGBq1qyZIwIsZiYFJk+ebMaPH28/29auXWvq1KljWrdubc4++2x7qVChgq/F4TjRFxMTRQl888035u9//7v54osv7GfXqlWrTOPGje1vjAsvvNCcdNJJUa/wf/ett94yL7/8sn3B73//e/u97f/VTJmPAr/4xS/Mv/71L3PLLbeYiy++2Ncq6pjv9ddft9+r+h28ceNG07RpU3P00Uebrl27mosuushUqlSp1Hl9/vnnRvvk119/bZYvX26/k/U7ulOnTvY7unr16qXOI0wTlDv8bQnTArEs+S2wY8cOM3jwYLNixQpTu3ZtAtD83txZXbvVq1ebxx57zHzyySdxl6N8+fL2w/+6664z1apVizsdTyCQSoEHHnjAvPfee3aWffr0Mffdd18qZ8+8ELACM2fONI888ojRZ2G8oqDgtttuI2CKB8TjvgWSPb7761//an73u9+ZgwcPxnyvunXrml/96lfm1FNPjfk8DxaegE4e/r//9//MrFmz4q689pf/+7//M1WqVIk7DceJcWl4ohSBdevWmbvvvtsGn/EmPf300829996bcB+M9dqlS5ean/70p0YBq8rIkSPtCaFY0/JYYQgofHziiSfsyvoNQP/yl7+Yl156yezcuTMukoJQVcLo3r17zGn2799vhg8fbv72t7+ZeJFh/fr1jcLZXr16xZxHGB+s8OtvSxgXjGXKPwHVerr11lvNkiVL7MrpoOTKK6/MvxVljbIuoFpP+oKYP3++XRb9gDr//PPND37wA1vjSfd1FuvQoUNGZ7XWr19vdKBCQSDdApMmTTIvvPBC5G1UW+V//ud/Ive5gUAqBPTj7Oc//7nZtGmTnV2HDh1M//79zbnnnmvP/Ot7WAe2ulYIwD6YCvXCnUcyx3f6MfXggw+aUaNG2R9Wap3Ro0cPc9lll5lu3bqZPXv2mA0bNpi9e/eaqVOnmjPOOMPW8CtcZdZcAgcOHLA/2OfMmWNBjjrqKHPppZeavn372tpIW7ZsMbqoooVOAunYLlYIynEi+1OyAvptoe9X1fhUadKkif19od8ZqgH61Vdf2RM6anWhffG0007z/Vb6XlaYpM8+V1RLT79bKIUpoFaMjz76aGTlTz75ZPtZF3kgxo0JEybYSkAuRO/Zs6fdR8855xxbS141Q7du3Wq2b99uPvjgA/s5WVRUVGJOzzzzjHnzzTft46opqpaTupxwwgl2H9fxo76rP/zwQ7ufKwzNhUIT+FzYSnmwjPPmzbM1UfRlQEEg3QJPP/10JGjXDyrVFFCNY2+5/PLLbc0nHZyoNp4OUAgBvELcTrWAgvZhw4alerbMD4ESAqr97s76qynezTffbLzNQS+55BLzs5/9zKxZs8b84x//MBMnTjRnnXVWifnwAAKlCSR7fDdu3LhITXjVQlEtk4YNG0beTt/Rf/jDH8yrr75qf2ANHTrU6LudUtgCI0aMMOpqQUU/wh9++GHjbX6pAF0133Vcp+m0D8XqZobjxMLej5JdewVKasWjAEnlhhtusE2AvfNTQKTvXJ2AfOedd8yZZ57pu7uZ559/3ixevNg7O24XqIBO0rz44ou2CXu82pexaBRKut8aOu5TTfjoSj6DBg2yLS8Ubmqf1jTa93Qi0hV9t6v5vIq6LVRXDGr27opOqqt28rPPPmtPTGker7zyilHryrCX8C9h2AVZvoQCOiugg1r90CL8TEjFkykSUO0A9cmjog/su+66q0T4qefatm1rbr/9dt20hf5onQTX6RDQwctDDz1k1EyU7hbSIcw8nYAOZl3XH6odpZoq3vBT0zVo0MCeAHKvUf+LFASCCJTl+E77qGqWqKh2npr2ecNPPa4fUeoySeGoisIsnbCkFK6Avkfd8Z32GzVB9oafktEPeG+3HuojVPubt3Cc6NXgdhABNUVWX4oqOrmoPoqji/ovVjDqit/vV9VYfu211+zLOE50eoV5re49fvSjH9kTgGqtGKSoRqe+n1W0f0aHn3pcn5M6NlTrIBWF7q7/bfvAt3/U36gr1157bbHw0z2ulrzqB1RFLStV8z4XCgFoLmylHF1GNRG45pprjPp30kFLuXLlzFVXXWXq1auXo2vEYueCgD7A1WRORf2RJNrf1EdU1apV7bTRH/z2Qf4gkCIBnUX99NNP7eegDjpc0eciBYFUCqj/MNefYufOneN2cK8+n9yZej7/UrkF8n9eZT2++/jjjyM1qFRjr1GjRjHRFNyfd955dgAQ/cjKlR9XMVeGB8ssoC6LXNNgDeAWHZq7N1AoqpPcKgo/NUCSt3Cc6NXgdhABBeoqNWrUMD/5yU/ivlShk5rGt2nTJvKbJO7E3z6hk+OqWarfy2q5dsopp0Qm5zgxQpH3N7T9f/Ob39hw0gXtGrjX76BHAvL2jaxuGeIVfb9+97vfjTy9cOHCyG3d8N6P10eo9k3VxHclV44lj9RzdUvONQIpEtCoimpep6I+IdQRtP5J3JcHH+gpgmY2xQTUF5lGXtRId+3bty/2XPQd/fhXnyfaT9UXig6UK1euHD0Z9xEok4DOrD733HN2Hjobq5EXKQhkQmD37t1x30a1CnSwraJ+xygI+BUo6/Gdul1w5fvf/767GfNaJ851oSCgEzpvv/227WMxulujaB332afjvOj+EzlOjNbivh8BjX6t/j1V1G1WnTp14r5MNTjfeOONuM9HP/H444/b8QjUck2DvmlgOErhCei4bPTo0ZEV19gVGtNC3RS5Ulp+ooo/OmGoUD3eyUU3L2+fne7kknvOe63PzHjFW0M1V44lCUDjbU0eT4mADlB0dl9nLnS2jIJAugV05lQXP0V95LmQXmfYCD/9qDFNEAGF6uoXRwcFqpGiGgNunwsyH6ZFwK+Amgyreaj6j1JNPZ3ciQ4ANK/p06dHAtB27dr5nT3TIWAFynJ852qJqIuGZs2aIYqAbwH9uE/Uskcz0ijxLqhSaBodlnKc6JubCT0C7nNLD3lrvXkmSeqmBqxRs2UVDYCkLmoohS3wne98xzaB79KlS2AIdf3mt3j7m1VtZW/RcaFOdqroWp+l0UXdiXibyufKsSQBaPSW5H7KBPr06WNHeY/unydlb8CMECijwN/+9rfIHDp27Bi5zQ0EUiWgARjUJFmjJ95zzz1xmyOn6v2YDwL6ztWosRo8Rid51KxOA8GpZokr+iGnfhdVVJsgVj9mblquEYgWKMvxnYJ517TP/VhSDRINuKCme7NnzzaqvacWHMcdd5zRe7muGqKXg/sIRAuon1gNeuS6ATn77LOjJwl0n+PEQFx5PfGSJUsi6+c+uzTQkbo30ufWggULbLN3fXZpUNXmzZtHpo93Q90zqMmzivZVfd5RCldA33X63RArbEy1ir5nFb67Ev07WMeRqsWs/kRHjRpl+wvt3bu3m9xW7HjqqaciJ5vU+vLYY4+NPB/mGwSgYd46Ob5s0WcScnx1WPw8E1AT+T/+8Y92rRQAJOonJc9WndXJkMCMGTPMX/7yF/tu1113nTnmmGMy9M68TaELaH/TDzMd3OrsvAJOHZyqZokGJNRjCp3UEb7CgmRqGRS6cSGvf1mO7/SD33W9oCakqkGikN7VgHKuCkQ1Qq1GUVaNFtUWpSAQS0DBuZon68SORn9XyK7+7TQStwaqSbZwnJisXH6+TqNru6LPrjlz5tjBVL3Ng9Vv4ocffmh/X9x0002mb9++7iUlrvUdrM8+vV6fb7feemuJaXigsAT0ezQT4adUX3755cjAgqoV37Rp02LYqmmvrhnuuOMO25z+zjvvtMeKCjkVimrQLvc/oSb3au2WK4UANFe2FMuJAAIpE1CtKDUzcQct/fv3JwBImS4zksD27dvNgw8+aDHUebi6AqEgkCkBBZuqcdytWzfz2GOP2Wbw3j6ktBxqFj98+HCC+UxtFN7HCri+GXVH3c787//+rw3kFVi1atXK1ppSDVGNKKtafJ999pn58Y9/bF555RXbnzyMCEQL/PrXv7YnfLyPDxo0qEzhJ8eJXk1uS8D72aUTNA899JAdO0Dfpa7mm0L4bdu22YGPHn30UTsAl0bQjlXUSkM1R1UULnlbacSanscQSJXAuHHjbCshzU+thm6//faYsz7++OPNn//8Z/ubWfu2Qn9dvEXdHCrsz6WWGgSg3i3IbQQQyHsB1QxQB+Oufyj1l6faUhQEUimgA1/VwFPfx6q9VFqn5al8b+aFgBtR1vXfJJGaNWvaQd9WrVpla3+qb9AhQ4aYn/70p7bJPGoIZELAGyKodqdCTjUnVWDfunXryCKouamCLXUhokDht7/9rbnvvvsiz3MDAQnomE7ftaqtpFpJuqi88MILtlaxuv8IWmOZ40RLyJ8oAe9n1/3332+f1ffnwIEDbY1jPaDPM52s0UU1PNV0WM3ao/dBhUnPP/+8nYcqYZx44on2Nn8QSLfA1KlTjX6juKLwMt5gSe+//75RM3d1LaKi3zKqKapjTFX0UFFLDbUsUojfsGFD+1jY/5QP+wKyfAgggECqBPSDX02i1FxKRU1Ohg0bZgcMSdV7MB8EdGZVTaBUNHpjvAMLpBBIh4BqLt14442Rzut1Bl8/xv7+97/bM/k6oFUor9omOohVX6B/+tOf0rEozBOBEgLeUWIVFqhbBo147A0/9SJ1GaLQU8G9yqRJkyLf3fYB/iDwrYBqu+uzTaPDq/n7yJEjTc+ePa2NwnOd5HEnvP2AcZzoR6kwp1F3Ha7os+v6668311xzTST81HOqya4a6wpFVTSdPse8RQG7mgvrOdV6Hzx4sPdpbiOQNgH9PtHxn/Y9Fe2/Gmk+VtEJSu2nCj81sKaCUnWr9Nprr5l3333XvP7666ZXr172pZ988on9rF2/fn2sWYXuMQLQ0G0SFggBBNIhoCZ1OljRqMgqTZo0sWe1ovs8Scd7M8/CEVB/OE8++aRd4e9+97vm3HPPLZyVZ01DIaCDUv3wV1G/nwqXvP3Pqtmx9kvVPqlWrZqdTrWlVqxYYW/zB4F0CkQPjHnDDTfYJnix3lNNS12QoOddc9FY0/JYYQoocHIhuWontWzZ0p7YPuussyyITgg9++yzvnA4TvTFVLATue9LAaj1mGpuxitXXXWVUT+hKnPnzo0ETrr/9NNP2z5rte+q5rvCJQoC6RZ46aWXzMMPPxzZF9VNiGowxyo6Oa791BVVFtL+XrVqVfeQrQmq+fXr188+pt8/zz33XOT5MN8gAA3z1mHZEEAgJQIKPfUjS00/VTRCo0bZI/xMCS8z+a+AzqiqWZSa4NWuXdsG7jqIiL64vmf1MtWGcs97m1eBikCyAm+99ZZ9qcIAdV4fr1+mZs2amZ/85Cd2WjXVcwN2Jfu+vA4BPwLRAWjXrl0Tvkw1mF1Rv6AUBPwIaEAZFyxNnz7daECjRIXjxEQ6PCcB72eXPpcUYMYrCorcSPE6znO/Pz7++GMzevRo+7LLL7/cVsZwx4Dea29NeR0zuue8tVDjvTePI+AV0D6joHLEiBH2YR0TahwM1VSOV1TT0/1WUQ1R9Scfr/zsZz+L9M+tFkabN2+ON2loHqcP0NBsChYEAQTSITB58mTbb9g333xjZ3/yySfbKv3eM7npeF/mWXgC6qdOHeOrqG8cHdyWVrR/6qKiAwz1tUNBIFkBNVVy/TI1b97cNG7cOOGs1FRUNURV1OciBYF0C3j7CFOAUNro7tqPXVENPQoCfgR0ElL9Ln7++ed2cgVQ6m4hVuE4MZYKj0ULeD+7/FSg0ElGNQ1W0f7XokUL89FHH0Vmq/5BdSmteMcpUHN6DaxJQcCPgELMu+++O7IfKphX39qnnXZawperT09XSuufVr+nu3TpYv7xj3/Yfm91olJ9Moe5EICGeeuwbAggUCYB9Qn1+OOPm8OHD9v5qJq++mRMdNa2TG/IixFAAIEsCrjwU4tQv379UpfE+yMuF87al7pCTBB6ATUL1b6pgWtUa17BgDfkjF4B1XxyhVGSnURhXqs5u/YXNbVUzeHSfmQXFRVFoNzgSJEH/nuD48RoEe7HE/AOZOQNiOJNz2dXPBkez4SA+jPWb17XB7K+d4cOHWpbQZb2/qrQ4Uq8E0fueV3n2rEkAah363EbAQTyRkAdPavPElc0KIifGnlueq4RCCqgM6tXX311qS/TgYV+dKmoA/zTTz/d3vYeQNgH+INAQAHVOKlUqZLtWkGjzCpgSnTCR2GCK+o7j4JAJgQ6depkpkyZYt9KteYTBaDeWp/qu5tSuALq3/jll1+2AH6O6VxfyHqBa45sX/zfPxwnejW4XZqAPrdcceMJuPuxrmN9dqkVmusbNNZr3GOqley6/LjgggsirymtVYd7PdeFLaDw/bbbbouEnxpk8LHHHvM9KKt3UMIvvvii1FrHOinlSi4cSxKAuq3FNQII5I2AmnI++uijdn3UD96dd97JYDR5s3XDuyLqH+raa68tdQGXL18eCUBVo8DPa0qdKRMg8K2ARkTWgevChQttX7T6kaamSfHKtGnTIk/FCggiT3IDgRQKaIAaF4B+8MEH5pxzzjH6ro5VNNqsK71793Y3uS5Age985zuRAFT7TaKT2vrscz/KVfMpukY8x4kFuAOVcZXbtm1rB9lS7U/tW9rHOnfuHHOuixYtMrqodOzYMbL/adRsN3J2zBf+90GdnHQB6KWXXmqbzyeanucQ8Aqo9aPb/zp06GBbQ6pbEL/Fezw4Y8YMM2DAgLgvVTP7WbNm2efdMWjciUPyBIMghWRDsBgIIJA6AX3wq+aTika5YyTu1NkyJwQQCLeAOqx3RX09uRDAPeauVTtKo4KqaGT4M8880z3FNQJpFVD/Y40aNbLvoUFB4o3SPXXq1EhQqr5CFYBRCldANfBcc8wFCxbEHbhNtZ8eeuihCFSsoJTjxAgPNwIIXHLJJZGpf/WrX5m1a9dG7rsb+/btM0888USk+60f/vCH7imuEUi7gPqd1QkiFZ34eeSRR+zArEHeWP3MutrGmt/zzz8f8+XqYm748OFG/c+r6OSmQtCwl/AvYdgFWT4EEAiVwPjx482cOXPsMqlGiTrAv/32230t41133RVpZuLrBUyEAAIIhEzgwgsvtKGRDlo3bNhgbr75ZnPVVVfZJkxHH320WbdunfnnP/9pXnjhBeMGh7v++uttzZaQrQqLk6cCGp1b38u//OUv7aAJGghEtUj69OljNLqy+gd97733zIsvvhgRuOOOOxJ25xCZkBt5K6Af1r/+9qTOTTfdZPebp59+2n6e9e/f3wbq+hE+c+ZMo4FitA+pnHTSSeayyy4rZsJxYjEO7gQQUHP0iRMnmtmzZ9vQR82MtX+deuqp9veDmgs/+eSTZvHixXau+jwjAA0AzKRlEti/f7/5zW9+E5mHWqap308/pUePHsYF/HqdfhPrs1Yh5x//+Ec7urv7jtb8VMP0ueeei9T+1ElN9TmaC4UANBe2EsuIAAK+BUaPHh2ZVh/aql3it+isLQUBBBDIZQGd+NGB67333mvmzp1ra6io7ycV1z+oWz9Ne9FFF0UOet3jXCOQbgH92FItKQVaCq7GjBljLwq5Dhw4EHl77aNDhgwxmp6CgAY/0kkdhZxq6aN+QXVRLXZ3QscpKfzUCMjah7yF40SvBreDCGhf0vgCqkGsIH3FihWR8QbU37ZrfaZ5qosjfb6VL0+D2yDGTJu8wGeffWb3STcH7Z+6+CnRg8p169bN3HrrrUYnmvT7eOzYsfai/fzQoUORGs6at8YwuO+++0yNGjX8vFXWp+E/MuubgAVAAIFUCrg+c1I5T+aFAAII5JKAmonqoFU17LwHtaod4Er79u3NM888Y8/YRwcEbhquEUinwAknnGC7YVCfeNWqVbNv5Q0/VXtKtVnUBx4FASegkzYaDEkBpyve8FOffzoJpIA9uu9PTc9xolPjOhkBDXip/Uu10r0DuLnws2bNmrZWqL5f1XUHBYFMCXgHfkvFe6pF0Z/+9Cdbw9kNqKn9XBWMVPS9rRZGqiGqvkZzpZT7dgX+swa5ssQsJwIIIIAAAggggIBvAdWw049+NQtVM/gWLVpEAiffM2FCBNIooJ8jqqmipqNqfqd9VLVKKAgkEti5c6f9bNOgMQ0bNrSDwBUVFSV6Cc8hkFIB7YMaeFDfs+o3UTU/FZJSEMgnAZ1AX7lypf28VfCpATcV8OfiCXQC0HzaM1kXBBBAAAEEEEAAAQQQQAABBBBAAAEEECgmQBP4YhzcQQABBBBAAAEEEEAAAQQQQAABBBBAAIF8EiAAzaetyboggAACCCCAAAIIIIAAAggggAACCCCAQDEBAtBiHNxBAAEEEEAAAQQQQAABBBBAAAEEEEAAgXwSIADNp63JuiCAAAIIIIAAAggggAACCCCAAAIIIIBAMQEC0GIc3EEAAQQQQAABBBBAAAEEEEAAAQQQQACBfBIgAM2nrcm6IIAAAggggAACCCCAAAIIIIAAAggggEAxAQLQYhzcQQABBBBAAAEEEEAAAQQQQAABBBBAAIF8EiAAzaetyboggAACCCCAAAIIIIAAAggggAACCCCAQDEBAtBiHNxBAAEEEEAAAQQQQAABBBBAAAEEEEAAgXwSIADNp63JuiCAAAIIIIAAAggggAACCCCAAAIIIIBAMYGKxe5xBwEEEEAAAQQQQAABBBBAoITAtm3bzLJly0y5cuXMcccdZ69LTMQDeSugba99oG7duqZFixZ5u56sGAIIIJCvAgSg+bplWS8EEEAgjwXWrVtnrr766pSv4ejRo0316tXtfF9++WXz6quv2tvHH3+8GTZsWMrfr9BniHGh7wHhWP8w7IcHDx40FSpUCAdIji9FOi0HDBhgxo0bZ374wx+ad955J8elsrv48bbTiy++aN544w27cN26dTNDhw7N7oJ63v2jjz4yAwcONLVr1zbz5883zZs39zzLTQQQQACBsAsQgIZ9C7F8CCCAAAIlBPbu3WsmTJhQ4vGyPnDgwIHILBYtWhR5jz179kQe50bqBDBOnSVzSl4gm/vh+vXrzR133GH69u1rLrroouRXgleadFuOHDnShp8VK1bkhFgZ9rfSttPChQsj373e7+QyvGXKXqoAfPjw4WbGjBnmhhtuIARPmSwzQgABBDIjQB+gmXHmXRBAAAEEEEAAAQQQiAj84Q9/MO3bt///7Z0L9BVV9cePiYKVGgJpSmFp+SAVQXwn6jLNpSktU2qllCG6Vi+Q5SvLVw+yfJBIkGagkgj21NQ0JCA1lUgzllqmmRjZU/BJSTX/8z3/5nRmfjP39Zt7Z37399lrXebMzJkz+3zOmTvc/dtnbyMP1CiK/HEKzRNoN0sZ7aZOneoUk+FL44Y0T6Dd49S8Rs1dodAHM2bMcBfdeuutZsGCBc01QG0IQAACECiVAB6gpeLn5hCAAAQg0AqBrbbaysycObPmpQ8//LDRUrpYpk2bZrbffvt4N3M7aNCgzOMchAAEIFA0gU9/+tNm3bp1RTfbL9trN8tPfvKT5u9//7t53eteZ84///x+ybiITrd7nIrQsV4bBxxwgPPYVgiEKVOmmHe/+91m2LBh9S7jPAQgAAEIVIAABtAKDAIqQAACEIBAcwQ233xzox+ktUTxPEMD6AknnGD22WefWpckzl1wwQVGP9YkxOZLoClsB8aFoaShXhBgHvYCXj+4VOFW4piUn/jEJ8zQoUP7Qa/pYi0C+s6QAfRvf/ubC2Exd+7cWtU5BwEIQAACFSHAEviKDARqQAACEIBAtQhsuumm5vWvf737bLbZZtVSrku0gXGXDGQf7wbzsI8PYJvVv/jii90d9IeweBl8m29J8xUnMGbMGHPIIYc4LRUb9g9/+EPFNUY9CEAAAhAQAQygzAMIQAACEIAABCAAAQhAAAIpAr/4xS/M0qVL3dGDDjrIbLPNNqka7PZXAhMmTHBd37BhQ92QPP2VEf2GAAQgUDUCLIGv2oigDwQgAAEIVIKAYoiuWrXK6bL11lu7OF+hYg899JB55JFH3KF9993X7Ljjjq68cuVKc8MNNxhlln7++efN8OHDzdixY83JJ59sBg8eHDZhXnrpJZdEYdmyZc6D5I1vfKPZeeedzZFHHmkUZ6xRefnll82KFSvML3/5SyO9n376adfOqFGjjD577723UfKGokX3uv32280TTzxhfve735k3vOENZtddd3WfkSNHunvXumczjNWHd7zjHa65NWvWmPvuu8/87Gc/c/1VbNfRo0c7zqrXjPzzn/803/ve98y9997ruL3wwgvmrW99q9lhhx3MEUcc4dg12l5Z4yD9NAaPPvqo+fWvf+0+2t9kk03cmOy2227m0EMPNTLg1JJwTsvDaZdddjH//ve/zYMPPuj4iPdzzz3n2Ij3SSedZF772tfmNll0e1k30nj96Ec/Mo899pjr/1//+lc3/3bffXejj8JevOY1tf/e38l5qGfz7rvvdl159dVXfZd++tOfmvXr17t9zb399tvPlUOG3fA9E/ant890syw97CYKl112ma99/PHH+3K6EPar7HEq4pkI+1OVcdJ3mr6nlYFd7+Ytt9zSvO1tbzPvf//73fuy0Xdcb99b8di/733vMx//+Mfdd+TVV19tPvvZz5otttgiPs0WAhCAAASqSMBmnUQgAAEIQAACXUfAGrWUVtl/7r///qb6eO655/pr3/Wud/W49qyzzvLn7Y+f6F//+ldkjZz+WHhvle1y+uiOO+7w7dgMstGQIUMy69sfctGkSZOiF1980dfPK1jDSTRixIjMdmIdDjvssMgu0ctrounjTz75ZGSNgzXvqXtbw29Ui3szjGfPnu30DK+J+xdurVEusoblun36z3/+E33pS1/KHYO4zWOOOSayxrW67ZUxDlLKGgMia9ysOxbqz5577hlZw0ZuX8I5femll0bWoFtznG0sxGjWrFkday+8kcbPxviN7B8navZdz67may0J51S9Z72383DhwoU19dU4feQjH/HqhmPSDd8zYX86zdJDbbDw+9//PhowYIAbL7v8PfrTn/6Ue2XYr7LGqchnIuxPp8cpvLe+29Svz3/+85H9Q0bus3PggQdGq1evzh0fnSjqvRXexC6D9zpZY3l4ijIEIAABCFSQQO0/idv/hSEQgAAEIAABCNQmIE8uLYebN29ebkV5e773ve913nnyEFVZWYWzxP5/wSVwuuiii7JOu2PW4OqSNB188MHOczGsqNilodx1113OG06ejr0VJX14z3veY+68885EU8qOrE8o8tSR56G8NXsr8kQ87bTTzPTp02s2NX/+fLPXXnsZazzOradz48ePd/zSY6AEW6HccsstZty4ccYaQ8LDvlzWOEgBJeGwBjvzk5/8xOujgjw/s+LWyqtLXqDWkJOon7Wzdu1aYw3niXGWB668rmLRXFBSGGuwiA/lbots7x//+IexBg9j/0hg/vznPyfuOXDgwMS+vC3lCXrVVVcljre6U+Q8bFaHbvueKZNlI+wXLVpk9HxL9NxoJUAjUsY4tfOZKHOc1q1bZ4466ihz3nnnGWsIzcV/zz33uO9Ceb9mSbveW/I+jUWxQBEIQAACEKg4gQoaZVEJAhCAAAQg0GsCnfQAtUvbnReITaYSffjDH47k5SWPS3k/Tpw40XuI2P8SOK9Ia6Rxx+zSwkh6yjPFLl+PLrnkksguofP11Z5d9pfJwibm8PXU7jvf+c7ILkePrEHP1Zf3kjW0Rm9605t8PWsYi+wS6cz2Gj0oj0jdT5+3vOUtkf3RF9kfnf5ya5CKvvjFL0aDBg3y9XRNljTjeTds2DDf3oknnhjZZc/O40cekGKR9oKt5Zl4yimn+LbUDxsmwLGzy6edmtqqD3Z5t69nl/Q7j8h0P8oaB7t0P+H9aEMtRHPmzHFeavKYkmgsrAHXeX7GY6atuGdJ6Hklj7f4Gru007Wla6wxxHmdar7F57WVN2Zaim4vbn/atGmJe9tl7tFtt93mdJR+NjRFNHPmTOd1Heq4ZMmSuInEtpPz0BqCIxtSwH3kFR7rp2c/Pm6Xdnv9Qobd8D0T9qe3z3SzLD3UBguhl/uMGTNqXhX2q4xxKvqZCPvT6XEK7x0/H9raP1pFX//619370oZciW666abIhovwz5DqnHHGGZnjVOR7K7yB3vOxjvJQjd+/YR3KEIAABCBQHQKmOqqgCQQgAAEIQKA4Ap00gMY/gLT0MUuOPfZY/yMprnvqqae6ZfPp+jLkhEv9str84x//GFlPRd+mln3LIJYlqisDUXxfG180q1pDx3QP61no25KhKU9kGI3vqWWkMsalpRnDk9oSlzCMQNielqnbGKT+njYWaXjal2281UghBmLdZKC23lP+fFi4+eabE3Wtd2l4OiprHKTE9ddf7/tgvTIjG/8zoVu4I6OgjO1xn7faaqvwtC+nDQ/iLSN6lsjobb2PfZtaim5j3iaqFt2eGl++fHni+bAx+CLrcZe4b7wjY/9OO+3kdVQ56zkpYx5Kx3C+fuc734nVTmzTDDWGWd8JuqgvfM+k+1PEM62+N8JS9RoVzZPwDyDW27rmpel+dXKc2vFMpPvTyXFK31ssv/CFL2TyV5gY/fEn/m57+9vf3qNe0e+t9A3CeZL3HKevYR8CEIAABMohgAG0HO7cFQIQgAAE2kyg0wZQGTTzREa7+AeatjbZUQ9jUXitTaLh659zzjnhKVeWl2ncnk0CUTfmpTxM5f0ZXyMjaytil7L7NuSpKsNansgLUT9MZRjbf//9I7tMu0fVZg1PtRir8SuvvNLrp77aZY897ikvopiDPJvqxQu1Sxx9fbv0P9FeWeMgJWySIq9XntdTqOyCBQt8/Tw2acODDesQNtGjbBORJAzE8roMpej2FGfXJqjy/ZCRu9YclC52Cbyvr37LszctZcxD6dCI0S7NsNYz0Be+Z5rpjxg18kw3ylL1GhXF9I2/J7SVh34taaZfRY5Tu56JZvojLkWOU/reNvRJLfTO8z0cq7QXZtHvrbQy4R9ZPvaxj6VPsw8BCEAAAhUiQAxQ+8ZEIAABCEAAAr0lcPbZZ+c2Yb1SEues12HNbLFxtnNd9NRTTyWuVYwz6/3nj33mM5/pEXvTn/xvQZlyjzvuOH/42muv9eVmCm9+85t9demhWKZ5ooy8ygyveJPK3GuTReRVbfi4+lpL4szZcR3FfQtFcfIWL17sD5155pl12SnuqGKeTp06NcGwzHFQLLzLL7/czJ0714iJdKwnyiweyiuvvBLu9ihbjy9z/vnn9zgeHrDL4I0yIcdiE3vFxR7bItqzhvzE82C9wupmeFes0KOPPtrrYw3BvtxqobfzsNX76rpu+54pk2WtcVCm8FDC777weF65U+PUqWeizHFSdvVaopjPoaTjOodj1473Vth+et6EelGGAAQgAIHyCWAALX8M0AACEIAABPo4AevRaWRkzJN08oy0MSp9XZjEaP369YnTv/3tb7V6wx874IADfLlWwS6D96fVRiuy3XbbGRsL018qQ65dfu8S5ci4mBYl4ylKlNwm/KGZ1e6QIUMSh5WMJBQZYm1mc39IiajqiRIB2XijxsYANDZ2qK9e5jjImKikLCeffLKREXDHHXf0emUVrJeksbHqEqc2bNiQ2E/vjB492lgPy/ThHvsyDsdil+KaNPP4XBHt2TAHcXMuyVNofPUnMgof+tCH/FEbU9eIR6tSxDxs9d7d9j1TJst6Y2DjAPsqSu5mw0b4/XqFTo5TJ56JMsdJ3/l2JUFN5Db+c+K8EieF0u73lo2F7W8Xzht/kAIEIAABCFSGwIDKaIIiEIAABCAAgT5KIG3gTHdDBqtQtt9++3C3R9nGzOxxLD4QGi/V7jXXXGPkbVlP5I0Zi4xArcrpp59uJk+e7I2wynyrj7KOj7PZ0m2MUfdJe722er/4Ov3IrNdPG4stru62cQbn+GDaCBj+cI3rNLotexzy9FRWdOn2+OOPm9/85jfGxmk1ypBs43MmLgmN6IkT/92pN0fja0KGNtaey8qeZaguor3Q2NNoe9LTLpuP1TXS0SYIM/X+COEvSBWKmIepJhve7bbvmTJZ1oMeGrKy5nOt6zs5Tp14JsocJxumpBZqd84m3EvUyfrjTjvfW+H8COdNQil2IAABCECgEgTyf2FVQj2UgAAEIAABCFSfgI2H2ZSSaUNdMxeHhjcthb700kubudzVtXEvzbPPPmtshvimr500aZKxSXec52fo9SlPVRvbzn2mTJlibFw088EPftB5TcoDp7ey7bbb9rYJZ5yLG5G3aF8eh7gf4q6QBgsXLjQ2TqHREs8ipFEDY2gA1X1lgA0NArEuRbRXhLFH+tjkSC0bQIuYhzGTZrfd9j1TJst67ENDVtZ8rnV9J8epE89EmePULMu8cWnneyv8DpT3qQywRa5+yOsTxyEAAQhAoHkCSZeU5q/nCghAAAIQgEC/J6Algp2SNWvWFHIrm8G85XZsYiDz85//3NgM3LlLQ+V9eOGFF5pRo0YZmwCp5XvFF2688cZxseWtDL+x2AQ0cbGlbRXGQTFY9ePbJt4wNmlLpvFT4RS0VLxeHL00hEY8r3SNPH9DkYdllhTR3l/+8hff9ODBg325XmHzzTdPVAnDICRONLBTxDxs4DaZVbrte6ZMlpmAg4PhHGlm+bua6OQ4deKZKHOc0qsngiFqutiu91Y4P+RZnw5b07SiXAABCEAAAm0jgAdo29DSMAQgAAEIQKB4AqE3kuKO2ozuLd2kUY+8vMaVAGfWrFnmiiuuMPfff7+LA3rnnXealStXGnmmxqJERIcffrjzUNQP0DIl7HNvDMDqQ9nj8MMf/tDYLPSJeJbbbLONGTt2rNl9993NHnvs4baKDyoDhrxDFS+0UVHyqkbkmWeeSVTLW/5bRHvhUvb0fRNKpHZWr16dONKoMTZxUT/bKXt+l41bcTxjWbt2bVys3JZnorkhacd7K5wfWo5flNdqcz2jNgQgAAEINEIAA2gjlKgDAQhAAAIQqAiBMEO8DDsy6qVjoHVSVRnXlIhJn8997nNGBk8ZQhWbdNmyZU4VJZ3RftkG0DBZkLx0lC04nTgpi52W+qcZlzkOWmY5YcIEb/yUN+v111/vsp3nxUlNJwapFwM0bTTM4qJj6XoywmZJul5WHR1L1wvbC+PKKo5no/L0008nqmIATeDI3Clzfmcq1OGDYXiQdFbxDqtS83Y8EzXx5J4s8r2ld14s4byJj7GFAAQgAIHqEGAJfHXGAk0gAAEIQAACdQmEhgkl+Vm1alXda1RBP+KffPJJF5+soQtyKsmYqTikeR54Q4cONcq6vXTpUrc0O25GS7TDmKHx8U5uQwOo7vvUU0/Vvb24KQu0DHH77befSy6ki8och7vvvjuxzPK73/2uUUb7POOn9E0ngKpnANVcaUQUTzOWnXfe2WjJfZYU0V5o7JEHb6PxTh999FGvkhKMhTH7/AkKCQJlzu+EIiXthIas0MBVkjq5t+WZyEWTONHO91ZoIA/nTUIBdiAAAQhAoBIEMIBWYhhQAgIQgAAEINAYAf3g3XTTTX3lOXPm+HKtwqc+9SkjA6A8GbU8Wj8Im5WPfvSjLnGQjCONxJTUEu1Y5HEZJnCKj3dyqx+nYezPq666qu7tlyxZ4pb0K7mP4poq7ICkzHFYvny511v9GTdunN/PK9x+++2JUzKe1xItmVdog1ryyiuvmJChYo3mSRHt7brrrt7Iq/l72WWX5d3OH3/11VfNV7/6Vb9/yCGHVGaJahjfsJ5B2negQ4Uy53crXSyaZWjIChMitaJbO6/pa89E0ePUCNt2v7fCOKyhx3ojulEHAhCAAAQ6SwADaGd5czcIQAACEIBArwgoc/kZZ5zh21AG8HvuucfvZxVkfFq0aJE7pficRx55pIsLmVW31rHRo0cbGZQkt956q1vuXqv+E0884U/LO1HL9csU6XDOOed4Fa677roeS679SVuQkTA0sh199NFGHoSSMsch1kF6yLAcxlzVsbTISH7jjTcmDuclKworKYlVLZk9e7aJjUNi+4EPfKBWddPb9mR8Pumkk/w9ZsyYYULjgz8RFK6++moTLoE//vjjg7PlFjWHYgmT7sTHytyWOb9b6XfRLEeOHOnVkKFff/yoovS1Z6LocWpkTNr93nrooYe8GooxikAAAhCAQHUJYACt7tigGQQgAAEIQCCTwLnnnuuT8Mhz7LjjjjNKipMlWqIsg2fs8SnvUWVvb0V0n9iD57nnnjMnnHCCSceWjNvVkuvQ2HjUUUeZdDbuuG4nt1OnTvVenBs2bDBHHHGEeeSRR3qooHOnn366WbFihTsno6OyrYdS1jjsueeeXg0ZMkMjrT/x34Jig2aN90svvZSu2mNfsVxPPfVUb/QOK3zta18z6n8sqqfkS7WkiPa+/OUvew/OF1980Rx44IHm4Ycfzrzt5ZdfbqZNm+bPyfN54sSJfr/sgkIrxPL973+/riE7rtupbVnzu5X+Fc1SnpWxt7f0qfdHplZ0LuqavvRMFD1OjTBs53tLYTh+9atfeTUUigSBAAQgAIHqEiAJUnXHBs0gAAEIQAACmQT0I1Leb/JmkwFUXnDHHHOMS4KjZET68a64dYoV+a1vfct5MqohGS9lEAszPGfeIOegloVOnz7dGzYV51NGglNOOcXdU5mTn332WSOPmHnz5hl5Tknk9dNMBvKc2xdyeODAgc5gGC/XloF4n332cd6Le+21l4v1+dhjj5n58+cbbWO55JJLzL777hvvum1Z4yCDtrxp40RAMlTJCHjiiSeaESNGGBk3FW5gwYIF5sc//rGbI4MHD3bzQEZDyZo1a9y23j/f+MY3zAMPPGCOPfZYI8Or2r3rrrvM4sWL/aXKOTLoowAACSlJREFURH3xxRf7/VqF3ranJaZKtiVDtkT6aFz0LIwdO9YMHz7cZbxX6IJ7773Xq6LYpAsXLjQa/6rILrvs4j0Lb775ZqMx2nbbbY3moeZf2VLW/G6l3+1gKWPWFVdc4dSRAXTSpEmtqNb2a/rSM9GOcaoHuJ3vrfvuu8//4ULP7pgxY+qpw3kIQAACECiRAAbQEuFzawhAAAIQgECrBOTVIkOUfpTHhjAtS9cnS+TBOGvWLJc9POt8o8fOPvtsI6Ohlt5L1q5da2QczBN5fWr5vbzvqiLjx483d9xxh1FsOCXT0fLjb37zm+6T1lFLuxU/NTa4pc+XMQ5bbLGFueGGG8zBBx/sklrJCC7jnj5Zsv/++7sl8Geeeaa56aabXBV5DCuTfJ7IkL7TTjuZuXPnOg+n0MspvGbvvfc2t9xySyK2ang+LhfZ3pQpU4yMGvLIVQISJdeSwTDPaKjlzN/+9reNkjRVSeSZe9ttt/nEZPIm00fex1WRMuZ3K31vB8vQABoa01vRr93X9JVnoh3j1Ajbdr23wnmhECl6XyAQgAAEIFBdAiyBr+7YoBkEIAABCECgJoFDDz3UZYHXj8qtt946s+4mm2zijKSPP/64Oe200zLrNHtQ3p3yLDzooIMyL5WnqYxNup9i58ljsWqipe+rVq1yXpNhYqRQT2V9l/djmEQnPB+XyxgHGTW1dF/G3CxRqAMZJ2V8liewMp8ffvjhvuoPfvADZ/j1B1IFeUrKKDxz5kznVZk67drT0vtly5blzr3wmqLbU/gF9V9G3Lzx0xyUjgpjIM+zqslhhx3mvHTTz67+oNFIjNZO9aeM+d1s39rBUt9vW265pVNFnsZVjQMas+oLz0Q7xinuf71tO95b4R8ctQoDgQAEIACBahPYyHoNRNVWEe0gAAEIQAACEGiEgJa9yygkY+ewYcPc8nQtUdfy33aJsqOvXr3afeQNquX38vYMY721695Ftqs+yMtR4QSU5V4GsyFDhrR0i06Pg8ZACaf0GTRokNltt91cH8JkSY10RF5SX/nKV1xVGb20jDwWJRKSt9PGG29slCFc8T7rtV90e7EuWdtnnnnGGbQVe3a77bZzc7+KRs8s3RWfV2Mnb9ahQ4e6EBWbbbZZVtVKHOv0/G6m00WzPOuss7yHuzyo4+ejGZ3KqlvlZ6LocWqWcRHvrQcffNAved9hhx2cgVzfjwgEIAABCFSXAAbQ6o4NmkEAAhCAAAQgAIGOEahlsGxFiaLba0UHroFAbwjIUKYYt+vXr3eezjIqyqsegYBWXsyePduBuOaaayobI5aRggAEIACB/xFgCfz/WFCCAAQgAAEIQAACEIAABCDgCCg8weTJk11ZxtBwyTOI+i8BGcSVZE6i8CITJ07svzDoOQQgAIE+RAADaB8aLFSFAAQgAAEIQAACEIAABDpHQEvfFVNXMmfOnM7dmDtVlsCNN95o1q1b5/STpztewZUdKhSDAAQgkCCAATSBgx0IQAACEIAABCAAAQhAAAL/T2D48OFm2rRpbmfx4sVm6dKloOnHBJSg7KKLLnIEFPN60qRJ/ZgGXYcABCDQtwhgAO1b44W2EIAABCAAAQhAAAIQgEAHCVx44YVm5MiR7o5KjEQO2Q7Cr9itrrzySpf0TwmPrr32WjNw4MCKaYg6EIAABCCQRwADaB4ZjkMAAhCAAAQgAAEIQAAC/Z6AjFzXXXedGTBggFm5cqVZtGhRv2fSHwGsXbvWTJ8+3XVdS9/Hjh3bHzHQZwhAAAJ9lsCAPqs5ikMAAhCAAAQgAAEIFEZg/PjxLqGHGtSy395K0e31Vh+uh0BvCIwZM8YtfZ43b57z/JswYYLZaKONetMk1/YxAvPnzzdDhgwxo0aNMhdccEEf0x51IQABCEBgI7uEIwIDBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFuJMAS+G4cVfoEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIOAIYQJkIEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQNcSwADatUNLxyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEMoMwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoGsJYADt2qGlYxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAGUOYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0LUEMIB27dDSMQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAADKHMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ6FoCGEC7dmjpGAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIABlDkAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIdC0BDKBdO7R0DAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMAAyhyAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEupYABtCuHVo6BgEIQAACEIAABCAAAQhAAAIQgAAEIAABCGAAZQ5AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACXUsAA2jXDi0dgwAEIAABCEAAAhCAAAQgAAEIQAACEIAABDCAMgcgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBriWAAbRrh5aOQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhhAmQMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA1xLAANq1Q0vHIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQygzAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgawlgAO3aoaVjEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAZQ5gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQtQQwgHbt0NIxCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAMocwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDoWgIYQLt2aOkYBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggAGUOQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQh0LQEMoF07tHQMAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQwADKHIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAS6lsD/AVE+FXtZBxSpAAAAAElFTkSuQmCC" width="672" /></p>
<div class="sourceCode" id="cb370"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb370-1"><a href="#cb370-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureB4a_Model1.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">4</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p>Model 4</p>
<div class="sourceCode" id="cb372"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb372-1"><a href="#cb372-1" tabindex="-1"></a>calcNPHint <span class="ot">&lt;-</span> <span class="cf">function</span>(fit, <span class="at">tfunc =</span> <span class="st">&quot;id&quot;</span>, <span class="at">seed =</span> <span class="dv">12345</span>, <span class="at">nsims =</span> <span class="dv">1000</span>){</span>
<span id="cb372-2"><a href="#cb372-2" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;id&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) t}</span>
<span id="cb372-3"><a href="#cb372-3" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;log&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) <span class="fu">log</span>(t)}</span>
<span id="cb372-4"><a href="#cb372-4" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;sqrt&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) <span class="fu">sqrt</span>(t)}</span>
<span id="cb372-5"><a href="#cb372-5" tabindex="-1"></a>  <span class="fu">set.seed</span>(seed)</span>
<span id="cb372-6"><a href="#cb372-6" tabindex="-1"></a>  simb <span class="ot">&lt;-</span> MASS<span class="sc">::</span><span class="fu">mvrnorm</span>(<span class="at">n =</span> nsims, <span class="at">mu =</span> <span class="fu">coef</span>(fit), <span class="at">Sigma =</span> <span class="fu">vcov</span>(fit))</span>
<span id="cb372-7"><a href="#cb372-7" tabindex="-1"></a>  col_need <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">colnames</span>(simb) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;tt(crisis_mon)&quot;</span>, </span>
<span id="cb372-8"><a href="#cb372-8" tabindex="-1"></a>                                          <span class="st">&quot;fm_gender&quot;</span>, <span class="st">&quot;gender_crisis&quot;</span>, </span>
<span id="cb372-9"><a href="#cb372-9" tabindex="-1"></a>                                          <span class="st">&quot;tt(gender_crisis)&quot;</span>))</span>
<span id="cb372-10"><a href="#cb372-10" tabindex="-1"></a>  simb <span class="ot">&lt;-</span> simb[, col_need]</span>
<span id="cb372-11"><a href="#cb372-11" tabindex="-1"></a>  time <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="at">from =</span> <span class="dv">1</span>, <span class="at">to =</span> <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">3</span>)</span>
<span id="cb372-12"><a href="#cb372-12" tabindex="-1"></a>  xmat_men <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="fu">ft</span>(time), <span class="dv">0</span>)</span>
<span id="cb372-13"><a href="#cb372-13" tabindex="-1"></a>  xmat_women <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">1</span>, <span class="fu">ft</span>(time), <span class="fu">ft</span>(time))</span>
<span id="cb372-14"><a href="#cb372-14" tabindex="-1"></a>  xb_men <span class="ot">&lt;-</span> <span class="fu">exp</span>(xmat_men <span class="sc">%*%</span> <span class="fu">t</span>(simb))</span>
<span id="cb372-15"><a href="#cb372-15" tabindex="-1"></a>  xb_women <span class="ot">&lt;-</span> <span class="fu">exp</span>(xmat_women <span class="sc">%*%</span> <span class="fu">t</span>(simb))</span>
<span id="cb372-16"><a href="#cb372-16" tabindex="-1"></a>  cut_points <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.025</span>, <span class="fl">0.05</span>, <span class="fl">0.5</span>, <span class="fl">0.95</span>, <span class="fl">0.975</span>)</span>
<span id="cb372-17"><a href="#cb372-17" tabindex="-1"></a>  est_men <span class="ot">&lt;-</span> <span class="fu">apply</span>(xb_men, <span class="dv">1</span>, quantile, cut_points) <span class="sc">|&gt;</span> <span class="fu">t</span>()</span>
<span id="cb372-18"><a href="#cb372-18" tabindex="-1"></a>  est_men <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">time =</span> time, est_men) <span class="sc">|&gt;</span> <span class="fu">as.data.frame</span>()</span>
<span id="cb372-19"><a href="#cb372-19" tabindex="-1"></a>  est_women <span class="ot">&lt;-</span> <span class="fu">apply</span>(xb_women, <span class="dv">1</span>, quantile, cut_points) <span class="sc">|&gt;</span> <span class="fu">t</span>()</span>
<span id="cb372-20"><a href="#cb372-20" tabindex="-1"></a>  est_women <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">time =</span> time, est_women) <span class="sc">|&gt;</span> <span class="fu">as.data.frame</span>()</span>
<span id="cb372-21"><a href="#cb372-21" tabindex="-1"></a>  <span class="fu">colnames</span>(est_men) <span class="ot">&lt;-</span> <span class="fu">colnames</span>(est_women) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;time&quot;</span>, <span class="st">&quot;low95&quot;</span>, <span class="st">&quot;low90&quot;</span>, <span class="st">&quot;median&quot;</span>, <span class="st">&quot;upp90&quot;</span>, <span class="st">&quot;upp95&quot;</span>)</span>
<span id="cb372-22"><a href="#cb372-22" tabindex="-1"></a>  est_men <span class="ot">&lt;-</span> est_men <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">gender =</span> <span class="st">&quot;men&quot;</span>)</span>
<span id="cb372-23"><a href="#cb372-23" tabindex="-1"></a>  est_women <span class="ot">&lt;-</span> est_women <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">gender =</span> <span class="st">&quot;women&quot;</span>)</span>
<span id="cb372-24"><a href="#cb372-24" tabindex="-1"></a>  est <span class="ot">&lt;-</span> <span class="fu">rbind</span>(est_men, est_women)</span>
<span id="cb372-25"><a href="#cb372-25" tabindex="-1"></a>  <span class="fu">return</span>(est)</span>
<span id="cb372-26"><a href="#cb372-26" tabindex="-1"></a>}</span>
<span id="cb372-27"><a href="#cb372-27" tabindex="-1"></a></span>
<span id="cb372-28"><a href="#cb372-28" tabindex="-1"></a>calcNPHint2 <span class="ot">&lt;-</span> <span class="cf">function</span>(fit, <span class="at">tfunc =</span> <span class="st">&quot;id&quot;</span>, <span class="at">seed =</span> <span class="dv">12345</span>, <span class="at">nsims =</span> <span class="dv">1000</span>){</span>
<span id="cb372-29"><a href="#cb372-29" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;id&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) t}</span>
<span id="cb372-30"><a href="#cb372-30" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;log&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) <span class="fu">log</span>(t)}</span>
<span id="cb372-31"><a href="#cb372-31" tabindex="-1"></a>  <span class="cf">if</span> (tfunc <span class="sc">==</span> <span class="st">&quot;sqrt&quot;</span>){ft <span class="ot">&lt;-</span> <span class="cf">function</span>(t) <span class="fu">sqrt</span>(t)}</span>
<span id="cb372-32"><a href="#cb372-32" tabindex="-1"></a>  <span class="fu">set.seed</span>(seed)</span>
<span id="cb372-33"><a href="#cb372-33" tabindex="-1"></a>  simb <span class="ot">&lt;-</span> MASS<span class="sc">::</span><span class="fu">mvrnorm</span>(<span class="at">n =</span> nsims, <span class="at">mu =</span> <span class="fu">coef</span>(fit), <span class="at">Sigma =</span> <span class="fu">vcov</span>(fit))</span>
<span id="cb372-34"><a href="#cb372-34" tabindex="-1"></a>  col_need <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">colnames</span>(simb) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;crisis_mon&quot;</span>, <span class="st">&quot;tt(crisis_mon)&quot;</span>, </span>
<span id="cb372-35"><a href="#cb372-35" tabindex="-1"></a>                                          <span class="st">&quot;fm_gender&quot;</span>, <span class="st">&quot;gender_crisis&quot;</span>))</span>
<span id="cb372-36"><a href="#cb372-36" tabindex="-1"></a>  simb <span class="ot">&lt;-</span> simb[, col_need]</span>
<span id="cb372-37"><a href="#cb372-37" tabindex="-1"></a>  time <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="at">from =</span> <span class="dv">1</span>, <span class="at">to =</span> <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">3</span>)</span>
<span id="cb372-38"><a href="#cb372-38" tabindex="-1"></a>  xmat_men <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="fu">ft</span>(time))</span>
<span id="cb372-39"><a href="#cb372-39" tabindex="-1"></a>  xmat_women <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">1</span>, <span class="fu">ft</span>(time))</span>
<span id="cb372-40"><a href="#cb372-40" tabindex="-1"></a>  xb_men <span class="ot">&lt;-</span> <span class="fu">exp</span>(xmat_men <span class="sc">%*%</span> <span class="fu">t</span>(simb))</span>
<span id="cb372-41"><a href="#cb372-41" tabindex="-1"></a>  xb_women <span class="ot">&lt;-</span> <span class="fu">exp</span>(xmat_women <span class="sc">%*%</span> <span class="fu">t</span>(simb))</span>
<span id="cb372-42"><a href="#cb372-42" tabindex="-1"></a>  cut_points <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.025</span>, <span class="fl">0.05</span>, <span class="fl">0.5</span>, <span class="fl">0.95</span>, <span class="fl">0.975</span>)</span>
<span id="cb372-43"><a href="#cb372-43" tabindex="-1"></a>  est_men <span class="ot">&lt;-</span> <span class="fu">apply</span>(xb_men, <span class="dv">1</span>, quantile, cut_points) <span class="sc">|&gt;</span> <span class="fu">t</span>()</span>
<span id="cb372-44"><a href="#cb372-44" tabindex="-1"></a>  est_men <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">time =</span> time, est_men) <span class="sc">|&gt;</span> <span class="fu">as.data.frame</span>()</span>
<span id="cb372-45"><a href="#cb372-45" tabindex="-1"></a>  est_women <span class="ot">&lt;-</span> <span class="fu">apply</span>(xb_women, <span class="dv">1</span>, quantile, cut_points) <span class="sc">|&gt;</span> <span class="fu">t</span>()</span>
<span id="cb372-46"><a href="#cb372-46" tabindex="-1"></a>  est_women <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">time =</span> time, est_women) <span class="sc">|&gt;</span> <span class="fu">as.data.frame</span>()</span>
<span id="cb372-47"><a href="#cb372-47" tabindex="-1"></a>  <span class="fu">colnames</span>(est_men) <span class="ot">&lt;-</span> <span class="fu">colnames</span>(est_women) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;time&quot;</span>, <span class="st">&quot;low95&quot;</span>, <span class="st">&quot;low90&quot;</span>, <span class="st">&quot;median&quot;</span>, <span class="st">&quot;upp90&quot;</span>, <span class="st">&quot;upp95&quot;</span>)</span>
<span id="cb372-48"><a href="#cb372-48" tabindex="-1"></a>  est_men <span class="ot">&lt;-</span> est_men <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">gender =</span> <span class="st">&quot;men&quot;</span>)</span>
<span id="cb372-49"><a href="#cb372-49" tabindex="-1"></a>  est_women <span class="ot">&lt;-</span> est_women <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">gender =</span> <span class="st">&quot;women&quot;</span>)</span>
<span id="cb372-50"><a href="#cb372-50" tabindex="-1"></a>  est <span class="ot">&lt;-</span> <span class="fu">rbind</span>(est_men, est_women)</span>
<span id="cb372-51"><a href="#cb372-51" tabindex="-1"></a>  <span class="fu">return</span>(est)</span>
<span id="cb372-52"><a href="#cb372-52" tabindex="-1"></a>}</span>
<span id="cb372-53"><a href="#cb372-53" tabindex="-1"></a></span>
<span id="cb372-54"><a href="#cb372-54" tabindex="-1"></a>est_m4_3 <span class="ot">&lt;-</span> <span class="fu">calcNPHint2</span>(<span class="at">fit =</span> fmt_m4nph3, <span class="at">tfunc =</span> <span class="st">&quot;sqrt&quot;</span>)</span></code></pre></div>
<pre><code>## mutate: new variable &#39;gender&#39; (character) with one unique value and 0% NA
## mutate: new variable &#39;gender&#39; (character) with one unique value and 0% NA</code></pre>
<div class="sourceCode" id="cb374"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb374-1"><a href="#cb374-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> est_m4_3, <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> time)) <span class="sc">+</span> </span>
<span id="cb374-2"><a href="#cb374-2" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>) <span class="sc">+</span> </span>
<span id="cb374-3"><a href="#cb374-3" tabindex="-1"></a>  <span class="fu">geom_ribbon</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> low95, <span class="at">ymax =</span> upp95), <span class="at">alpha =</span> <span class="fl">0.5</span>) <span class="sc">+</span> </span>
<span id="cb374-4"><a href="#cb374-4" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> median)) <span class="sc">+</span> </span>
<span id="cb374-5"><a href="#cb374-5" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>(<span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">4</span>, <span class="dv">8</span>, <span class="dv">16</span>, <span class="dv">32</span>, <span class="dv">64</span>, <span class="dv">128</span>)) <span class="sc">+</span> </span>
<span id="cb374-6"><a href="#cb374-6" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Time since appointment (months)&quot;</span>, </span>
<span id="cb374-7"><a href="#cb374-7" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Hazard ratio for banking crisis&quot;</span>,</span>
<span id="cb374-8"><a href="#cb374-8" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;Model 4&quot;</span>) <span class="sc">+</span> </span>
<span id="cb374-9"><a href="#cb374-9" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb374-10"><a href="#cb374-10" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(gender <span class="sc">~</span> ., <span class="at">ncol =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb374-11"><a href="#cb374-11" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">18</span>),</span>
<span id="cb374-12"><a href="#cb374-12" tabindex="-1"></a>        <span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>), </span>
<span id="cb374-13"><a href="#cb374-13" tabindex="-1"></a>        <span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb374-14"><a href="#cb374-14" tabindex="-1"></a>        <span class="at">strip.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>))</span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb375"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb375-1"><a href="#cb375-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/FigureB4b_Model4.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">4</span>)</span></code></pre></div>
<p>When does the effect for women become greater than 1 at 95%
level?</p>
<div class="sourceCode" id="cb376"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb376-1"><a href="#cb376-1" tabindex="-1"></a>est_m4_3 <span class="sc">|&gt;</span> <span class="fu">filter</span>(gender <span class="sc">==</span> <span class="st">&quot;women&quot;</span> <span class="sc">&amp;</span> low95 <span class="sc">&gt;</span> <span class="dv">1</span>)</span></code></pre></div>
<pre><code>## filter: removed 59 rows (74%), 21 rows remaining</code></pre>
<pre><code>##    time    low95    low90   median    upp90    upp95 gender
## 1    58 1.001428 1.061259 1.514210 2.158144 2.276980  women
## 2    61 1.012880 1.082511 1.539773 2.202909 2.318712  women
## 3    64 1.022584 1.099463 1.567933 2.243555 2.363477  women
## 4    67 1.026227 1.112242 1.594609 2.287638 2.415022  women
## 5    70 1.043410 1.127477 1.621788 2.335807 2.469078  women
## 6    73 1.060554 1.140046 1.649899 2.376134 2.519224  women
## 7    76 1.068036 1.153764 1.675608 2.420765 2.561771  women
## 8    79 1.075423 1.168788 1.703213 2.469749 2.618704  women
## 9    82 1.082721 1.182889 1.727663 2.512206 2.671920  women
## 10   85 1.092380 1.194656 1.753575 2.557998 2.731211  women
## 11   88 1.103098 1.206765 1.781455 2.601939 2.788919  women
## 12   91 1.117627 1.219223 1.801256 2.646157 2.847896  women
## 13   94 1.137454 1.232451 1.825738 2.690157 2.895673  women
## 14   97 1.152997 1.246470 1.852768 2.734619 2.952190  women
## 15  100 1.161429 1.257841 1.879577 2.789222 3.009143  women
## 16  103 1.169797 1.268528 1.908749 2.839188 3.066094  women
## 17  106 1.183204 1.279936 1.933413 2.887929 3.118574  women
## 18  109 1.197557 1.291763 1.961760 2.939702 3.172544  women
## 19  112 1.207331 1.303129 1.987783 2.986616 3.222287  women
## 20  115 1.215379 1.315318 2.006583 3.035521 3.272305  women
## 21  118 1.221297 1.324847 2.034184 3.091079 3.325623  women</code></pre>
</div>
</div>
<div id="plot-hazard-ratio" class="section level2">
<h2>Plot Hazard Ratio</h2>
<p>Let’s re-parameterize the interactive models to obtain coefficients
for each combination of values.</p>
<div class="sourceCode" id="cb379"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb379-1"><a href="#cb379-1" tabindex="-1"></a>df <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">mutate</span>(</span>
<span id="cb379-2"><a href="#cb379-2" tabindex="-1"></a>  <span class="at">gender_cr =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;Men, Crisis=0&quot;</span>, <span class="st">&quot;NA&quot;</span>),</span>
<span id="cb379-3"><a href="#cb379-3" tabindex="-1"></a>  <span class="at">gender_cr =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;Men, Crisis=1&quot;</span>, gender_cr),</span>
<span id="cb379-4"><a href="#cb379-4" tabindex="-1"></a>  <span class="at">gender_cr =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women, Crisis=0&quot;</span>, gender_cr),</span>
<span id="cb379-5"><a href="#cb379-5" tabindex="-1"></a>  <span class="at">gender_cr =</span> <span class="fu">if_else</span>(crisis_mon <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women, Crisis=1&quot;</span>, gender_cr),</span>
<span id="cb379-6"><a href="#cb379-6" tabindex="-1"></a>  <span class="at">gender_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;Men, CrisisApp=0&quot;</span>, <span class="st">&quot;NA&quot;</span>),</span>
<span id="cb379-7"><a href="#cb379-7" tabindex="-1"></a>  <span class="at">gender_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;Men, CrisisApp=1&quot;</span>, gender_ap),</span>
<span id="cb379-8"><a href="#cb379-8" tabindex="-1"></a>  <span class="at">gender_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women, CrisisApp=0&quot;</span>, gender_ap),</span>
<span id="cb379-9"><a href="#cb379-9" tabindex="-1"></a>  <span class="at">gender_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women, CrisisApp=1&quot;</span>, gender_ap),</span>
<span id="cb379-10"><a href="#cb379-10" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, <span class="st">&quot;Men, A=0, C=0&quot;</span>, <span class="st">&quot;NA&quot;</span>),</span>
<span id="cb379-11"><a href="#cb379-11" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, </span>
<span id="cb379-12"><a href="#cb379-12" tabindex="-1"></a>                      <span class="st">&quot;Women, A=0, C=0&quot;</span>, gen_cr_ap),</span>
<span id="cb379-13"><a href="#cb379-13" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, </span>
<span id="cb379-14"><a href="#cb379-14" tabindex="-1"></a>                      <span class="st">&quot;Men, A=0, C=1&quot;</span>, gen_cr_ap),</span>
<span id="cb379-15"><a href="#cb379-15" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, </span>
<span id="cb379-16"><a href="#cb379-16" tabindex="-1"></a>                      <span class="st">&quot;Women, A=0, C=1&quot;</span>, gen_cr_ap),</span>
<span id="cb379-17"><a href="#cb379-17" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, </span>
<span id="cb379-18"><a href="#cb379-18" tabindex="-1"></a>                      <span class="st">&quot;Men, A=1, C=0&quot;</span>, gen_cr_ap),</span>
<span id="cb379-19"><a href="#cb379-19" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">0</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, </span>
<span id="cb379-20"><a href="#cb379-20" tabindex="-1"></a>                      <span class="st">&quot;Women, A=1, C=0&quot;</span>, gen_cr_ap),</span>
<span id="cb379-21"><a href="#cb379-21" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">0</span>, </span>
<span id="cb379-22"><a href="#cb379-22" tabindex="-1"></a>                      <span class="st">&quot;Men, A=1, C=1&quot;</span>, gen_cr_ap),</span>
<span id="cb379-23"><a href="#cb379-23" tabindex="-1"></a>  <span class="at">gen_cr_ap =</span> <span class="fu">if_else</span>(crisis_appoint <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> crisis_mon <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> fm_gender <span class="sc">==</span> <span class="dv">1</span>, </span>
<span id="cb379-24"><a href="#cb379-24" tabindex="-1"></a>                      <span class="st">&quot;Women, A=1, C=1&quot;</span>, gen_cr_ap),</span>
<span id="cb379-25"><a href="#cb379-25" tabindex="-1"></a> <span class="at">gender_cr_11 =</span> <span class="fu">if_else</span>(gender_cr <span class="sc">==</span> <span class="st">&quot;Women, Crisis=1&quot;</span>, <span class="st">&quot;1 Women, Crisis=1&quot;</span>, gender_cr),</span>
<span id="cb379-26"><a href="#cb379-26" tabindex="-1"></a> <span class="at">gender_ap_11 =</span> <span class="fu">if_else</span>(gender_ap <span class="sc">==</span> <span class="st">&quot;Women, CrisisApp=1&quot;</span>, <span class="st">&quot;1 Women, CrisisApp=1&quot;</span>, gender_ap)</span>
<span id="cb379-27"><a href="#cb379-27" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## mutate: new variable &#39;gender_cr&#39; (character) with 4 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;gender_ap&#39; (character) with 5 unique values and 8% NA</code></pre>
<pre><code>##         new variable &#39;gen_cr_ap&#39; (character) with 9 unique values and 8% NA</code></pre>
<pre><code>##         new variable &#39;gender_cr_11&#39; (character) with 4 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;gender_ap_11&#39; (character) with 5 unique values and 8% NA</code></pre>
<p>Estimate models that are equivalent but alternatively
parameterized.</p>
<div class="sourceCode" id="cb385"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb385-1"><a href="#cb385-1" tabindex="-1"></a>fmt_m4a <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> gender_cr))</span>
<span id="cb385-2"><a href="#cb385-2" tabindex="-1"></a>fmt_m4b <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m1, <span class="fu">update</span>(fml_tenure, . <span class="sc">~</span> gender_cr_11))</span>
<span id="cb385-3"><a href="#cb385-3" tabindex="-1"></a>fmt_m6a <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> gender_cr <span class="sc">+</span> .))</span>
<span id="cb385-4"><a href="#cb385-4" tabindex="-1"></a>fmt_m6b <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> gender_cr_11 <span class="sc">+</span> .))</span>
<span id="cb385-5"><a href="#cb385-5" tabindex="-1"></a>model_list_46 <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m4, fmt_m4a, fmt_m4b, fmt_m6, fmt_m6a, fmt_m6b)</span></code></pre></div>
<p>Check the equivalence</p>
<div class="sourceCode" id="cb386"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb386-1"><a href="#cb386-1" tabindex="-1"></a>model_list_46 <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m4a, fmt_m4b, fmt_m6a, fmt_m6b)</span>
<span id="cb386-2"><a href="#cb386-2" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_46,</span>
<span id="cb386-3"><a href="#cb386-3" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_46, get_clse_hr),</span>
<span id="cb386-4"><a href="#cb386-4" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb386-5"><a href="#cb386-5" tabindex="-1"></a>          <span class="at">keep =</span> <span class="fu">c</span>(<span class="st">&quot;gender_cr*&quot;</span>),</span>
<span id="cb386-6"><a href="#cb386-6" tabindex="-1"></a>          <span class="at">report =</span> (<span class="st">&quot;vc*p&quot;</span>),</span>
<span id="cb386-7"><a href="#cb386-7" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Men/noC&quot;</span>, <span class="st">&quot;Women/C&quot;</span>, <span class="st">&quot;Men/noC&quot;</span>, <span class="st">&quot;Women/C&quot;</span>),</span>
<span id="cb386-8"><a href="#cb386-8" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="4">
<em>Dependent variable:</em>
</td>
</tr>
<tr>
<td>
</td>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="4">
tenure_t0
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
Men/noC
</td>
<td>
Women/C
</td>
<td>
Men/noC
</td>
<td>
Women/C
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
gender_crMen, Crisis=1
</td>
<td>
0.480<sup>***</sup>
</td>
<td>
</td>
<td>
0.346<sup>***</sup>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
p = 0.000
</td>
<td>
</td>
<td>
p = 0.00003
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
gender_crWomen, Crisis=0
</td>
<td>
-0.074
</td>
<td>
</td>
<td>
0.129
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
p = 0.462
</td>
<td>
</td>
<td>
p = 0.296
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
gender_crWomen, Crisis=1
</td>
<td>
0.096
</td>
<td>
</td>
<td>
-0.005
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
p = 0.641
</td>
<td>
</td>
<td>
p = 0.980
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
gender_cr_11Men, Crisis=0
</td>
<td>
</td>
<td>
-0.096
</td>
<td>
</td>
<td>
0.005
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
p = 0.641
</td>
<td>
</td>
<td>
p = 0.980
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
gender_cr_11Men, Crisis=1
</td>
<td>
</td>
<td>
0.385<sup>*</sup>
</td>
<td>
</td>
<td>
0.351
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
p = 0.072
</td>
<td>
</td>
<td>
p = 0.105
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
gender_cr_11Women, Crisis=0
</td>
<td>
</td>
<td>
-0.170
</td>
<td>
</td>
<td>
0.134
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
p = 0.470
</td>
<td>
</td>
<td>
p = 0.574
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
99,670
</td>
<td>
99,670
</td>
<td>
64,057
</td>
<td>
64,057
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-21,498.050
</td>
<td>
-21,498.050
</td>
<td>
-13,747.280
</td>
<td>
-13,747.280
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="4" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Show exponentiated coefficients (Hazard ratio).</p>
<div id="figure-4" class="section level3">
<h3>Figure 4</h3>
<div class="sourceCode" id="cb387"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb387-1"><a href="#cb387-1" tabindex="-1"></a>df_hr <span class="ot">&lt;-</span> <span class="fu">summary</span>(fmt_m6a) <span class="sc">$</span> conf.int[<span class="fu">c</span>(<span class="st">&quot;gender_crMen, Crisis=1&quot;</span>, </span>
<span id="cb387-2"><a href="#cb387-2" tabindex="-1"></a>                                       <span class="st">&quot;gender_crWomen, Crisis=0&quot;</span>, </span>
<span id="cb387-3"><a href="#cb387-3" tabindex="-1"></a>                                       <span class="st">&quot;gender_crWomen, Crisis=1&quot;</span>),] <span class="sc">|&gt;</span> </span>
<span id="cb387-4"><a href="#cb387-4" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">|&gt;</span> </span>
<span id="cb387-5"><a href="#cb387-5" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">hr =</span> <span class="st">&quot;exp(coef)&quot;</span>, <span class="at">low =</span> <span class="st">&quot;lower .95&quot;</span>, <span class="at">up =</span> <span class="st">&quot;upper .95&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb387-6"><a href="#cb387-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gender =</span> <span class="fu">c</span>(<span class="st">&quot;Men&quot;</span>, <span class="st">&quot;Women&quot;</span>, <span class="st">&quot;Women&quot;</span>),</span>
<span id="cb387-7"><a href="#cb387-7" tabindex="-1"></a>         <span class="at">crisis =</span> <span class="fu">c</span>(<span class="st">&quot;Crisis = 1&quot;</span>, <span class="st">&quot;Crisis = 0&quot;</span>, <span class="st">&quot;Crisis = 1&quot;</span>), </span>
<span id="cb387-8"><a href="#cb387-8" tabindex="-1"></a>         <span class="at">gc =</span> <span class="fu">c</span>(<span class="st">&quot;Men</span><span class="sc">\n</span><span class="st">Crisis&quot;</span>, <span class="st">&quot;Women</span><span class="sc">\n</span><span class="st">No Crisis&quot;</span>, <span class="st">&quot;Women</span><span class="sc">\n</span><span class="st">Crisis&quot;</span>),</span>
<span id="cb387-9"><a href="#cb387-9" tabindex="-1"></a>         <span class="at">signif =</span> <span class="fu">if_else</span>((low <span class="sc">&gt;</span> <span class="dv">1</span> <span class="sc">&amp;</span> up <span class="sc">&gt;</span> <span class="dv">1</span>) <span class="sc">|</span> (low <span class="sc">&lt;</span> <span class="dv">1</span> <span class="sc">&amp;</span> up <span class="sc">&lt;</span> <span class="dv">1</span>), <span class="st">&quot;Yes&quot;</span>, <span class="st">&quot;No&quot;</span>),</span>
<span id="cb387-10"><a href="#cb387-10" tabindex="-1"></a>         <span class="at">mnum =</span> <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">2</span>,<span class="dv">1</span>))</span></code></pre></div>
<pre><code>## select: renamed 3 variables (hr, low, up) and dropped one variable</code></pre>
<pre><code>## mutate: new variable &#39;gender&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;crisis&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;gc&#39; (character) with 3 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;signif&#39; (character) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;mnum&#39; (double) with 3 unique values and 0% NA</code></pre>
<div class="sourceCode" id="cb394"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb394-1"><a href="#cb394-1" tabindex="-1"></a>df_hr</span></code></pre></div>
<pre><code>##                                 hr       low       up gender     crisis
## gender_crMen, Crisis=1   1.4128526 1.2027057 1.659718    Men Crisis = 1
## gender_crWomen, Crisis=0 1.1380735 0.8932306 1.450030  Women Crisis = 0
## gender_crWomen, Crisis=1 0.9948724 0.6661194 1.485876  Women Crisis = 1
##                                        gc signif mnum
## gender_crMen, Crisis=1        Men\nCrisis    Yes    3
## gender_crWomen, Crisis=0 Women\nNo Crisis     No    2
## gender_crWomen, Crisis=1    Women\nCrisis     No    1</code></pre>
<div class="sourceCode" id="cb396"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb396-1"><a href="#cb396-1" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> df_hr, </span>
<span id="cb396-2"><a href="#cb396-2" tabindex="-1"></a>       <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> hr, <span class="at">y =</span> <span class="fu">reorder</span>(gc, mnum))) <span class="sc">+</span> </span>
<span id="cb396-3"><a href="#cb396-3" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="dv">2</span>, <span class="at">alpha =</span> <span class="fl">0.4</span>) <span class="sc">+</span> </span>
<span id="cb396-4"><a href="#cb396-4" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> low, <span class="at">xend =</span> up, <span class="at">yend =</span> <span class="fu">reorder</span>(gc, mnum),</span>
<span id="cb396-5"><a href="#cb396-5" tabindex="-1"></a>                   <span class="at">color =</span> signif), <span class="at">size =</span> <span class="fl">1.5</span>) <span class="sc">+</span> </span>
<span id="cb396-6"><a href="#cb396-6" tabindex="-1"></a>  <span class="fu">geom_label</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">round</span>(hr, <span class="dv">2</span>), <span class="at">color =</span> signif)) <span class="sc">+</span> </span>
<span id="cb396-7"><a href="#cb396-7" tabindex="-1"></a>  <span class="fu">xlim</span>(<span class="fl">0.5</span>, <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb396-8"><a href="#cb396-8" tabindex="-1"></a>  <span class="fu">scale_color_grey</span>(<span class="at">start =</span> <span class="fl">0.6</span>, <span class="at">end =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb396-9"><a href="#cb396-9" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span> </span>
<span id="cb396-10"><a href="#cb396-10" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;&quot;</span>, <span class="at">x =</span> <span class="st">&quot;Hazard ratio (men under no crisis as baseline)&quot;</span>) <span class="sc">+</span> </span>
<span id="cb396-11"><a href="#cb396-11" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb396-12"><a href="#cb396-12" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">1.625</span>, <span class="at">xend =</span> <span class="fl">1.625</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb396-13"><a href="#cb396-13" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">1.575</span>, <span class="at">xend =</span> <span class="fl">1.625</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb396-14"><a href="#cb396-14" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">1.575</span>, <span class="at">xend =</span> <span class="fl">1.625</span>, <span class="at">y =</span> <span class="dv">2</span>, <span class="at">yend =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb396-15"><a href="#cb396-15" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="fl">1.525</span>,  <span class="at">y =</span> <span class="fl">1.5</span>, <span class="at">label =</span> <span class="st">&quot;p = 0.57&quot;</span>, <span class="at">angle =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb396-16"><a href="#cb396-16" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">1.875</span>, <span class="at">xend =</span> <span class="fl">1.875</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb396-17"><a href="#cb396-17" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">1.825</span>, <span class="at">xend =</span> <span class="fl">1.875</span>, <span class="at">y =</span> <span class="dv">1</span>, <span class="at">yend =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb396-18"><a href="#cb396-18" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="at">x =</span> <span class="fl">1.825</span>, <span class="at">xend =</span> <span class="fl">1.875</span>, <span class="at">y =</span> <span class="dv">3</span>, <span class="at">yend =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb396-19"><a href="#cb396-19" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="fl">1.775</span>,  <span class="at">y =</span> <span class="fl">2.5</span>, <span class="at">label =</span> <span class="st">&quot;p = 0.11&quot;</span>, <span class="at">angle =</span> <span class="dv">0</span>) <span class="sc">+</span> </span>
<span id="cb396-20"><a href="#cb396-20" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>), </span>
<span id="cb396-21"><a href="#cb396-21" tabindex="-1"></a>        <span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>))</span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb397"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb397-1"><a href="#cb397-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;Figures/Figure4_TenureByGender.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">4</span>)</span></code></pre></div>
</div>
</div>
<div id="outlier-analysis" class="section level2">
<h2>Outlier Analysis</h2>
<div class="sourceCode" id="cb398"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb398-1"><a href="#cb398-1" tabindex="-1"></a>spell_lev <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb398-2"><a href="#cb398-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_name <span class="sc">!=</span> <span class="st">&quot;Unknown&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb398-3"><a href="#cb398-3" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb398-4"><a href="#cb398-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">time =</span> <span class="fu">max</span>(tenure_t), </span>
<span id="cb398-5"><a href="#cb398-5" tabindex="-1"></a>            <span class="at">fail =</span> <span class="fu">max</span>(tenure_d), </span>
<span id="cb398-6"><a href="#cb398-6" tabindex="-1"></a>            <span class="at">decade =</span> <span class="fu">max</span>(decade), <span class="co"># decade of termination</span></span>
<span id="cb398-7"><a href="#cb398-7" tabindex="-1"></a>            <span class="at">year =</span> <span class="fu">max</span>(year),     <span class="co"># year of termination</span></span>
<span id="cb398-8"><a href="#cb398-8" tabindex="-1"></a>            <span class="at">decade0 =</span> <span class="fu">min</span>(decade),    <span class="co"># decade of appointment</span></span>
<span id="cb398-9"><a href="#cb398-9" tabindex="-1"></a>            <span class="at">year0 =</span> <span class="fu">min</span>(year),    <span class="co"># year of appointment</span></span>
<span id="cb398-10"><a href="#cb398-10" tabindex="-1"></a>            <span class="at">fm_gender =</span> <span class="fu">mean</span>(fm_gender), </span>
<span id="cb398-11"><a href="#cb398-11" tabindex="-1"></a>            <span class="at">fm_gender_f =</span> <span class="fu">if_else</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Woman&quot;</span>, <span class="st">&quot;Man&quot;</span>), </span>
<span id="cb398-12"><a href="#cb398-12" tabindex="-1"></a>            <span class="at">left_trunc =</span> <span class="fu">min</span>(left_trunc),</span>
<span id="cb398-13"><a href="#cb398-13" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb398-14"><a href="#cb398-14" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 3,257 rows and 11 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb402"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb402-1"><a href="#cb402-1" tabindex="-1"></a>spell_lev <span class="sc">|&gt;</span> <span class="fu">filter</span>(time <span class="sc">&gt;</span> <span class="dv">120</span> <span class="sc">&amp;</span> fm_gender_f <span class="sc">==</span> <span class="st">&quot;Woman&quot;</span>)</span></code></pre></div>
<pre><code>## filter: removed 3,253 rows (&gt;99%), 4 rows remaining</code></pre>
<pre><code>## # A tibble: 4 × 11
##   spellid  time  fail decade  year decade0 year0 fm_gender fm_gender_f
##     &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;   &lt;dbl&gt; &lt;dbl&gt;     &lt;dbl&gt; &lt;chr&gt;      
## 1     637  120.     1   2010  2015    2010  2015         1 Woman      
## 2     728  180.     1   1990  1995    1990  1995         1 Woman      
## 3    2070  142.     1   2010  2015    2010  2015         1 Woman      
## 4    2385  223.     1   1990  1998    1990  1998         1 Woman      
## # ℹ 2 more variables: left_trunc &lt;dbl&gt;, right_cens_max &lt;dbl&gt;</code></pre>
<p>Longest serving woman (223.3 months)</p>
<div class="sourceCode" id="cb405"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb405-1"><a href="#cb405-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> <span class="fu">filter</span>(spellid <span class="sc">==</span> <span class="dv">2385</span>) <span class="sc">|&gt;</span> </span>
<span id="cb405-2"><a href="#cb405-2" tabindex="-1"></a>  <span class="fu">select</span>(fm_name, fm_gender, country_name) <span class="sc">|&gt;</span> <span class="fu">unique</span>()</span></code></pre></div>
<pre><code>## filter: removed 100,289 rows (&gt;99%), 223 rows remaining</code></pre>
<pre><code>## select: dropped 133 variables (iso3c, date, fwfm72_st, fwfm72_d, fwfm72_t, …)</code></pre>
<pre><code>## # A tibble: 1 × 3
##   fm_name      fm_gender country_name
##   &lt;chr&gt;            &lt;dbl&gt; &lt;chr&gt;       
## 1 Ki-chong Yun         1 Korea, North</code></pre>
<p>Second longest serving woman (180.2 months)</p>
<div class="sourceCode" id="cb409"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb409-1"><a href="#cb409-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> <span class="fu">filter</span>(spellid <span class="sc">==</span> <span class="dv">728</span>) <span class="sc">|&gt;</span> </span>
<span id="cb409-2"><a href="#cb409-2" tabindex="-1"></a>  <span class="fu">select</span>(fm_name, fm_gender, country_name) <span class="sc">|&gt;</span> <span class="fu">unique</span>()</span></code></pre></div>
<pre><code>## filter: removed 100,332 rows (&gt;99%), 180 rows remaining</code></pre>
<pre><code>## select: dropped 133 variables (iso3c, date, fwfm72_st, fwfm72_d, fwfm72_t, …)</code></pre>
<pre><code>## # A tibble: 1 × 3
##   fm_name              fm_gender country_name
##   &lt;chr&gt;                    &lt;dbl&gt; &lt;chr&gt;       
## 1 Mary Eugenia Charles         1 Dominica</code></pre>
<p>Third longest serving woman (142.1 months)</p>
<div class="sourceCode" id="cb413"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb413-1"><a href="#cb413-1" tabindex="-1"></a>df <span class="sc">|&gt;</span> <span class="fu">filter</span>(spellid <span class="sc">==</span> <span class="dv">2070</span>) <span class="sc">|&gt;</span> </span>
<span id="cb413-2"><a href="#cb413-2" tabindex="-1"></a>  <span class="fu">select</span>(fm_name, fm_gender, country_name) <span class="sc">|&gt;</span> <span class="fu">unique</span>()</span></code></pre></div>
<pre><code>## filter: removed 100,370 rows (&gt;99%), 142 rows remaining</code></pre>
<pre><code>## select: dropped 133 variables (iso3c, date, fwfm72_st, fwfm72_d, fwfm72_t, …)</code></pre>
<pre><code>## # A tibble: 1 × 3
##   fm_name                    fm_gender country_name
##   &lt;chr&gt;                          &lt;dbl&gt; &lt;chr&gt;       
## 1 Saara Kuugongelwa-Amadhila         1 Namibia</code></pre>
<ul>
<li>2385 (longest serving one in PRK) is not in the data for the full
model<br />
</li>
<li>728 (second longest serving one in DMA) is also not in the data for
the full model<br />
</li>
<li>2070 (third longest serving one in NAM)</li>
</ul>
<div class="sourceCode" id="cb417"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb417-1"><a href="#cb417-1" tabindex="-1"></a>cox_o1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m6, <span class="at">data =</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(spellid <span class="sc">!=</span> <span class="dv">2070</span>))</span></code></pre></div>
<pre><code>## filter: removed 984 rows (1%), 99,528 rows remaining
## filter: removed 984 rows (1%), 99,528 rows remaining</code></pre>
<div class="sourceCode" id="cb419"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb419-1"><a href="#cb419-1" tabindex="-1"></a>cox_o2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m6, <span class="at">data =</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(iso3c <span class="sc">!=</span> <span class="st">&quot;NAM&quot;</span>))</span></code></pre></div>
<pre><code>## filter: removed 334 rows (&lt;1%), 100,178 rows remaining</code></pre>
<pre><code>## filter: removed 334 rows (&lt;1%), 100,178 rows remaining</code></pre>
<div class="sourceCode" id="cb422"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb422-1"><a href="#cb422-1" tabindex="-1"></a>model_list_tblB7 <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m6, cox_o1, cox_o2)</span>
<span id="cb422-2"><a href="#cb422-2" tabindex="-1"></a>model_list_tblB7 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB7, add_AIC)</span></code></pre></div>
<div id="table-b7" class="section level3">
<h3>Table B7</h3>
<div class="sourceCode" id="cb423"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb423-1"><a href="#cb423-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB7,</span>
<span id="cb423-2"><a href="#cb423-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB7, get_clse_hr),</span>
<span id="cb423-3"><a href="#cb423-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb423-4"><a href="#cb423-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb423-5"><a href="#cb423-5" tabindex="-1"></a>                               <span class="st">&quot;Woman Minister&quot;</span>,</span>
<span id="cb423-6"><a href="#cb423-6" tabindex="-1"></a>                               <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb423-7"><a href="#cb423-7" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb423-8"><a href="#cb423-8" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>,</span>
<span id="cb423-9"><a href="#cb423-9" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb423-10"><a href="#cb423-10" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>,</span>
<span id="cb423-11"><a href="#cb423-11" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span></span>
<span id="cb423-12"><a href="#cb423-12" tabindex="-1"></a>                               ),</span>
<span id="cb423-13"><a href="#cb423-13" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb423-14"><a href="#cb423-14" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb423-15"><a href="#cb423-15" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B7: Tenure Model Robustness Check – Dropping Outliers, 1972-2017&quot;</span>,</span>
<span id="cb423-16"><a href="#cb423-16" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb423-17"><a href="#cb423-17" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB7.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B7: Tenure Model Robustness Check – Dropping Outliers,
1972-2017</strong>
</caption>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="3">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="3" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
0.346<sup>***</sup>
</td>
<td>
0.347<sup>***</sup>
</td>
<td>
0.346<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.082)
</td>
<td>
(0.082)
</td>
<td>
(0.082)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Minister
</td>
<td>
0.129
</td>
<td>
0.186
</td>
<td>
0.185
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.124)
</td>
<td>
(0.115)
</td>
<td>
(0.115)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
-0.480<sup>*</sup>
</td>
<td>
-0.537<sup>**</sup>
</td>
<td>
-0.536<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.252)
</td>
<td>
(0.247)
</td>
<td>
(0.247)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
-0.056
</td>
<td>
-0.063
</td>
<td>
-0.063
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.138)
</td>
<td>
(0.138)
</td>
<td>
(0.138)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.115<sup>**</sup>
</td>
<td>
0.118<sup>**</sup>
</td>
<td>
0.118<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.212<sup>***</sup>
</td>
<td>
0.213<sup>***</sup>
</td>
<td>
0.213<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.045
</td>
<td>
-0.040
</td>
<td>
-0.040
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.021<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
0.011
</td>
<td>
0.011
</td>
<td>
0.011
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.039
</td>
<td>
-0.038
</td>
<td>
-0.038
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.017
</td>
<td>
-0.019
</td>
<td>
-0.019
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
64,057
</td>
<td>
63,918
</td>
<td>
63,805
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-13,747.280
</td>
<td>
-13,743.340
</td>
<td>
-13,721.460
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
27,520.550
</td>
<td>
27,512.690
</td>
<td>
27,468.920
</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="3" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Calculate Ns</p>
<div class="sourceCode" id="cb424"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb424-1"><a href="#cb424-1" tabindex="-1"></a>calc_NAM0 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
<span id="cb424-2"><a href="#cb424-2" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb424-3"><a href="#cb424-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb424-4"><a href="#cb424-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(inflate_lag) <span class="sc">&amp;</span> </span>
<span id="cb424-5"><a href="#cb424-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb424-6"><a href="#cb424-6" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb424-7"><a href="#cb424-7" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb424-8"><a href="#cb424-8" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb424-9"><a href="#cb424-9" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 36,455 rows (36%), 64,057 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,287 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb428"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb428-1"><a href="#cb428-1" tabindex="-1"></a><span class="fu">table</span>(calc_NAM0 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  209 2078</code></pre>
<div class="sourceCode" id="cb430"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb430-1"><a href="#cb430-1" tabindex="-1"></a>calc_NAM1 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
<span id="cb430-2"><a href="#cb430-2" tabindex="-1"></a>                           spellid <span class="sc">!=</span> <span class="dv">2070</span> <span class="sc">&amp;</span> </span>
<span id="cb430-3"><a href="#cb430-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb430-4"><a href="#cb430-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb430-5"><a href="#cb430-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(inflate_lag) <span class="sc">&amp;</span> </span>
<span id="cb430-6"><a href="#cb430-6" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb430-7"><a href="#cb430-7" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb430-8"><a href="#cb430-8" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb430-9"><a href="#cb430-9" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb430-10"><a href="#cb430-10" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 36,594 rows (36%), 63,918 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,286 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb434"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb434-1"><a href="#cb434-1" tabindex="-1"></a><span class="fu">table</span>(calc_NAM1 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  208 2078</code></pre>
<div class="sourceCode" id="cb436"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb436-1"><a href="#cb436-1" tabindex="-1"></a>calc_NAM2 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
<span id="cb436-2"><a href="#cb436-2" tabindex="-1"></a>                           iso3c <span class="sc">!=</span> <span class="st">&quot;NAM&quot;</span> <span class="sc">&amp;</span> </span>
<span id="cb436-3"><a href="#cb436-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb436-4"><a href="#cb436-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb436-5"><a href="#cb436-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(inflate_lag) <span class="sc">&amp;</span> </span>
<span id="cb436-6"><a href="#cb436-6" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb436-7"><a href="#cb436-7" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb436-8"><a href="#cb436-8" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb436-9"><a href="#cb436-9" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb436-10"><a href="#cb436-10" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 36,707 rows (37%), 63,805 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,282 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb440"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb440-1"><a href="#cb440-1" tabindex="-1"></a><span class="fu">table</span>(calc_NAM2 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  207 2075</code></pre>
<p>Average tenure for men and women in Namibia</p>
<div class="sourceCode" id="cb442"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb442-1"><a href="#cb442-1" tabindex="-1"></a>spell_NAM <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> </span>
<span id="cb442-2"><a href="#cb442-2" tabindex="-1"></a>  <span class="fu">filter</span>(fm_name <span class="sc">!=</span> <span class="st">&quot;Unknown&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb442-3"><a href="#cb442-3" tabindex="-1"></a>  <span class="fu">filter</span>(iso3c <span class="sc">==</span> <span class="st">&quot;NAM&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb442-4"><a href="#cb442-4" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb442-5"><a href="#cb442-5" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">time =</span> <span class="fu">max</span>(tenure_t), </span>
<span id="cb442-6"><a href="#cb442-6" tabindex="-1"></a>            <span class="at">fail =</span> <span class="fu">max</span>(tenure_d), </span>
<span id="cb442-7"><a href="#cb442-7" tabindex="-1"></a>            <span class="at">decade =</span> <span class="fu">max</span>(decade), <span class="co"># decade of termination</span></span>
<span id="cb442-8"><a href="#cb442-8" tabindex="-1"></a>            <span class="at">year =</span> <span class="fu">max</span>(year),     <span class="co"># year of termination</span></span>
<span id="cb442-9"><a href="#cb442-9" tabindex="-1"></a>            <span class="at">decade0 =</span> <span class="fu">min</span>(decade),    <span class="co"># decade of appointment</span></span>
<span id="cb442-10"><a href="#cb442-10" tabindex="-1"></a>            <span class="at">year0 =</span> <span class="fu">min</span>(year),    <span class="co"># year of appointment</span></span>
<span id="cb442-11"><a href="#cb442-11" tabindex="-1"></a>            <span class="at">fm_gender =</span> <span class="fu">mean</span>(fm_gender), </span>
<span id="cb442-12"><a href="#cb442-12" tabindex="-1"></a>            <span class="at">fm_gender_f =</span> <span class="fu">if_else</span>(fm_gender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Woman&quot;</span>, <span class="st">&quot;Man&quot;</span>), </span>
<span id="cb442-13"><a href="#cb442-13" tabindex="-1"></a>            <span class="at">left_trunc =</span> <span class="fu">min</span>(left_trunc),</span>
<span id="cb442-14"><a href="#cb442-14" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb442-15"><a href="#cb442-15" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 842 rows (1%), 99,670 rows remaining</code></pre>
<pre><code>## filter: removed 99,336 rows (&gt;99%), 334 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 6 rows and 11 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb447"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb447-1"><a href="#cb447-1" tabindex="-1"></a>spell_NAM <span class="sc">|&gt;</span> <span class="fu">filter</span>(fm_gender_f <span class="sc">==</span> <span class="st">&quot;Woman&quot;</span>)</span></code></pre></div>
<pre><code>## filter: removed 5 rows (83%), one row remaining</code></pre>
<pre><code>## # A tibble: 1 × 11
##   spellid  time  fail decade  year decade0 year0 fm_gender fm_gender_f
##     &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;   &lt;dbl&gt; &lt;dbl&gt;     &lt;dbl&gt; &lt;chr&gt;      
## 1    2070  142.     1   2010  2015    2010  2015         1 Woman      
## # ℹ 2 more variables: left_trunc &lt;dbl&gt;, right_cens_max &lt;dbl&gt;</code></pre>
<div class="sourceCode" id="cb450"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb450-1"><a href="#cb450-1" tabindex="-1"></a>spell_NAM <span class="sc">|&gt;</span> <span class="fu">filter</span>(fm_gender_f <span class="sc">==</span> <span class="st">&quot;Man&quot;</span> <span class="sc">&amp;</span> fail <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span> <span class="fu">summarise</span>(<span class="at">time =</span> <span class="fu">mean</span>(time))</span></code></pre></div>
<pre><code>## filter: removed 2 rows (33%), 4 rows remaining</code></pre>
<pre><code>## summarise: now one row and one column, ungrouped</code></pre>
<pre><code>## # A tibble: 1 × 1
##    time
##   &lt;dbl&gt;
## 1  39.8</code></pre>
</div>
</div>
<div id="control-for-election-months-crisis-appointment-and-decade-fe" class="section level2">
<h2>Control for Election Months, Crisis Appointment, and Decade FE</h2>
<div class="sourceCode" id="cb454"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb454-1"><a href="#cb454-1" tabindex="-1"></a>fmt_m7 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> </span>
<span id="cb454-2"><a href="#cb454-2" tabindex="-1"></a>                                  gender_crisis <span class="sc">+</span> elec_leg <span class="sc">+</span> elec_exe <span class="sc">+</span> .))</span>
<span id="cb454-3"><a href="#cb454-3" tabindex="-1"></a>fmt_m8 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> </span>
<span id="cb454-4"><a href="#cb454-4" tabindex="-1"></a>                                  gender_crisis <span class="sc">+</span> crisis_appoint <span class="sc">+</span> .))</span>
<span id="cb454-5"><a href="#cb454-5" tabindex="-1"></a>fmt_m6_FE <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m6, <span class="fu">update</span>(fml_tenure_cov, . <span class="sc">~</span> crisis_mon <span class="sc">+</span> fm_gender <span class="sc">+</span> </span>
<span id="cb454-6"><a href="#cb454-6" tabindex="-1"></a>                                     gender_crisis <span class="sc">+</span> . <span class="sc">+</span> <span class="fu">as.factor</span>(decade80)))</span>
<span id="cb454-7"><a href="#cb454-7" tabindex="-1"></a></span>
<span id="cb454-8"><a href="#cb454-8" tabindex="-1"></a>model_list_tblB8 <span class="ot">&lt;-</span> <span class="fu">list</span>(fmt_m6, fmt_m7, fmt_m8, fmt_m6_FE)</span>
<span id="cb454-9"><a href="#cb454-9" tabindex="-1"></a>model_list_tblB8 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB8, add_AIC)</span></code></pre></div>
<div id="table-b8" class="section level3">
<h3>Table B8</h3>
<p>Show hazard rate coefficients.</p>
<div class="sourceCode" id="cb455"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb455-1"><a href="#cb455-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB8,</span>
<span id="cb455-2"><a href="#cb455-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB8, get_clse_hr),</span>
<span id="cb455-3"><a href="#cb455-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb455-4"><a href="#cb455-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Banking Crisis&quot;</span>, </span>
<span id="cb455-5"><a href="#cb455-5" tabindex="-1"></a>                               <span class="st">&quot;Woman Minister&quot;</span>,</span>
<span id="cb455-6"><a href="#cb455-6" tabindex="-1"></a>                               <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb455-7"><a href="#cb455-7" tabindex="-1"></a>                               <span class="st">&quot;Legislative Election&quot;</span>, <span class="st">&quot;Executive Election&quot;</span>,</span>
<span id="cb455-8"><a href="#cb455-8" tabindex="-1"></a>                               <span class="st">&quot;Crisis Appointment&quot;</span>,</span>
<span id="cb455-9"><a href="#cb455-9" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb455-10"><a href="#cb455-10" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>,</span>
<span id="cb455-11"><a href="#cb455-11" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb455-12"><a href="#cb455-12" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>,</span>
<span id="cb455-13"><a href="#cb455-13" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span>, </span>
<span id="cb455-14"><a href="#cb455-14" tabindex="-1"></a>                               <span class="st">&quot;1990s&quot;</span>, <span class="st">&quot;2000s&quot;</span>, <span class="st">&quot;2010s&quot;</span></span>
<span id="cb455-15"><a href="#cb455-15" tabindex="-1"></a>                               ),</span>
<span id="cb455-16"><a href="#cb455-16" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb455-17"><a href="#cb455-17" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb455-18"><a href="#cb455-18" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B8: Tenure Model Robustness Checks - Additional Controls, 1972-2017&quot;</span>,</span>
<span id="cb455-19"><a href="#cb455-19" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>, </span>
<span id="cb455-20"><a href="#cb455-20" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB8.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B8: Tenure Model Robustness Checks - Additional Controls,
1972-2017</strong>
</caption>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="4">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="4" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
0.346<sup>***</sup>
</td>
<td>
0.334<sup>***</sup>
</td>
<td>
0.374<sup>***</sup>
</td>
<td>
0.322<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.082)
</td>
<td>
(0.081)
</td>
<td>
(0.096)
</td>
<td>
(0.083)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Minister
</td>
<td>
0.129
</td>
<td>
0.125
</td>
<td>
0.142
</td>
<td>
0.105
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.124)
</td>
<td>
(0.121)
</td>
<td>
(0.125)
</td>
<td>
(0.126)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
-0.480<sup>*</sup>
</td>
<td>
-0.461<sup>*</sup>
</td>
<td>
-0.484<sup>*</sup>
</td>
<td>
-0.481<sup>*</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.252)
</td>
<td>
(0.248)
</td>
<td>
(0.254)
</td>
<td>
(0.251)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Legislative Election
</td>
<td>
</td>
<td>
1.270<sup>***</sup>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.104)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Executive Election
</td>
<td>
</td>
<td>
0.170
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.191)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis Appointment
</td>
<td>
</td>
<td>
</td>
<td>
-0.035
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
(0.083)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.017<sup>***</sup>
</td>
<td>
-0.017<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
-0.056
</td>
<td>
-0.053
</td>
<td>
-0.074
</td>
<td>
-0.057
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.138)
</td>
<td>
(0.136)
</td>
<td>
(0.140)
</td>
<td>
(0.140)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.115<sup>**</sup>
</td>
<td>
0.110<sup>**</sup>
</td>
<td>
0.109<sup>**</sup>
</td>
<td>
0.098<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
<td>
(0.051)
</td>
<td>
(0.050)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.212<sup>***</sup>
</td>
<td>
0.199<sup>***</sup>
</td>
<td>
0.196<sup>***</sup>
</td>
<td>
0.206<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
<td>
(0.038)
</td>
<td>
(0.038)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.045
</td>
<td>
-0.050
</td>
<td>
-0.047
</td>
<td>
-0.018
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
<td>
(0.047)
</td>
<td>
(0.047)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.022<sup>***</sup>
</td>
<td>
-0.022<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
0.011
</td>
<td>
0.012
</td>
<td>
0.010
</td>
<td>
0.011
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
<td>
(0.019)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.039
</td>
<td>
-0.041
</td>
<td>
-0.029
</td>
<td>
-0.027
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.027)
</td>
<td>
(0.026)
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
<td>
(0.001)
</td>
<td>
(0.0005)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.017
</td>
<td>
-0.017
</td>
<td>
-0.014
</td>
<td>
-0.027
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.019)
</td>
<td>
(0.018)
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
1990s
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.213<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.061)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
2000s
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.182<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.065)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
2010s
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.184<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.073)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
64,057
</td>
<td>
64,057
</td>
<td>
61,544
</td>
<td>
64,057
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-13,747.280
</td>
<td>
-13,674.430
</td>
<td>
-13,559.530
</td>
<td>
-13,740.580
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
27,520.550
</td>
<td>
27,378.860
</td>
<td>
27,147.050
</td>
<td>
27,513.150
</td>
</tr>
<tr>
<td colspan="5" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="4" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Calculate Ns for the crisis appoint model</p>
<div class="sourceCode" id="cb456"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb456-1"><a href="#cb456-1" tabindex="-1"></a>calc_n_3 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(crisis_appoint) <span class="sc">&amp;</span></span>
<span id="cb456-2"><a href="#cb456-2" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb456-3"><a href="#cb456-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb456-4"><a href="#cb456-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(inflate_lag) <span class="sc">&amp;</span> </span>
<span id="cb456-5"><a href="#cb456-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb456-6"><a href="#cb456-6" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb456-7"><a href="#cb456-7" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb456-8"><a href="#cb456-8" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb456-9"><a href="#cb456-9" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 38,968 rows (39%), 61,544 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,250 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb460"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb460-1"><a href="#cb460-1" tabindex="-1"></a><span class="fu">table</span>(calc_n_3 <span class="sc">$</span> fail) <span class="co"># 2250 - 2044</span></span></code></pre></div>
<pre><code>## 
##    0    1 
##  206 2044</code></pre>
</div>
</div>
<div id="currency-inflation-and-unemployment-crises" class="section level2">
<h2>Currency, Inflation, and Unemployment Crises</h2>
<div class="sourceCode" id="cb462"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb462-1"><a href="#cb462-1" tabindex="-1"></a>fml_tenure_cov <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(tenure_t0, tenure_t, tenure_d) <span class="sc">~</span> </span>
<span id="cb462-2"><a href="#cb462-2" tabindex="-1"></a>                            womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb462-3"><a href="#cb462-3" tabindex="-1"></a>                            presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb462-4"><a href="#cb462-4" tabindex="-1"></a>                            growth_lag <span class="sc">+</span> inflate_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb462-5"><a href="#cb462-5" tabindex="-1"></a>                            tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag)</span>
<span id="cb462-6"><a href="#cb462-6" tabindex="-1"></a>fml_tenure_inf <span class="ot">&lt;-</span> <span class="fu">formula</span>(<span class="fu">Surv</span>(tenure_t0, tenure_t, tenure_d) <span class="sc">~</span> </span>
<span id="cb462-7"><a href="#cb462-7" tabindex="-1"></a>                            womenrep_lag <span class="sc">+</span> leadgender <span class="sc">+</span> </span>
<span id="cb462-8"><a href="#cb462-8" tabindex="-1"></a>                            presidential <span class="sc">+</span> xm_qudsest <span class="sc">+</span> unified_aut <span class="sc">+</span> </span>
<span id="cb462-9"><a href="#cb462-9" tabindex="-1"></a>                            growth_lag <span class="sc">+</span> loggdppc_lag <span class="sc">+</span> </span>
<span id="cb462-10"><a href="#cb462-10" tabindex="-1"></a>                            tradepergdp_i_lag <span class="sc">+</span> kaopen_i_lag)</span>
<span id="cb462-11"><a href="#cb462-11" tabindex="-1"></a></span>
<span id="cb462-12"><a href="#cb462-12" tabindex="-1"></a><span class="do">## Define interaction terms</span></span>
<span id="cb462-13"><a href="#cb462-13" tabindex="-1"></a>df <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">mutate</span>(</span>
<span id="cb462-14"><a href="#cb462-14" tabindex="-1"></a> <span class="at">gender_currency =</span> currency_mon0 <span class="sc">*</span> fm_gender, </span>
<span id="cb462-15"><a href="#cb462-15" tabindex="-1"></a> <span class="at">gender_crisis20 =</span> crisis20 <span class="sc">*</span> fm_gender,</span>
<span id="cb462-16"><a href="#cb462-16" tabindex="-1"></a> <span class="at">gender_unemp =</span> cri_avgsd5_roll <span class="sc">*</span> fm_gender</span>
<span id="cb462-17"><a href="#cb462-17" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## mutate: new variable &#39;gender_currency&#39; (double) with 2 unique values and 0% NA</code></pre>
<pre><code>##         new variable &#39;gender_crisis20&#39; (double) with 3 unique values and 4% NA</code></pre>
<pre><code>##         new variable &#39;gender_unemp&#39; (double) with 3 unique values and 56% NA</code></pre>
<div class="sourceCode" id="cb466"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb466-1"><a href="#cb466-1" tabindex="-1"></a><span class="do">## Currency</span></span>
<span id="cb466-2"><a href="#cb466-2" tabindex="-1"></a>cox_c0 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(</span>
<span id="cb466-3"><a href="#cb466-3" tabindex="-1"></a>  fml_tenure_cov, . <span class="sc">~</span> fm_gender <span class="sc">+</span> crisis_mon <span class="sc">+</span> gender_crisis <span class="sc">+</span> .))</span>
<span id="cb466-4"><a href="#cb466-4" tabindex="-1"></a>cox_c1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(</span>
<span id="cb466-5"><a href="#cb466-5" tabindex="-1"></a>  fml_tenure_cov, . <span class="sc">~</span> fm_gender <span class="sc">+</span> currency_mon0 <span class="sc">+</span> gender_currency <span class="sc">+</span> .))</span>
<span id="cb466-6"><a href="#cb466-6" tabindex="-1"></a>cox_i0 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(</span>
<span id="cb466-7"><a href="#cb466-7" tabindex="-1"></a>  fml_tenure_inf, . <span class="sc">~</span> fm_gender <span class="sc">+</span> crisis_mon <span class="sc">+</span> gender_crisis <span class="sc">+</span> .))</span>
<span id="cb466-8"><a href="#cb466-8" tabindex="-1"></a>cox_i1 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(</span>
<span id="cb466-9"><a href="#cb466-9" tabindex="-1"></a>  fml_tenure_inf, . <span class="sc">~</span> fm_gender <span class="sc">+</span> crisis20 <span class="sc">+</span> gender_crisis20 <span class="sc">+</span> .))</span>
<span id="cb466-10"><a href="#cb466-10" tabindex="-1"></a>cox_u0 <span class="ot">&lt;-</span> <span class="fu">update</span>(fmt_m5, <span class="fu">update</span>(</span>
<span id="cb466-11"><a href="#cb466-11" tabindex="-1"></a>  fml_tenure_inf, . <span class="sc">~</span> fm_gender <span class="sc">+</span> crisis_mon <span class="sc">+</span> gender_crisis <span class="sc">+</span> .), </span>
<span id="cb466-12"><a href="#cb466-12" tabindex="-1"></a>  <span class="at">data =</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(cri_avgsd5_roll)))</span></code></pre></div>
<pre><code>## filter: removed 55,896 rows (56%), 44,616 rows remaining</code></pre>
<pre><code>## filter: removed 55,896 rows (56%), 44,616 rows remaining</code></pre>
<div class="sourceCode" id="cb469"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb469-1"><a href="#cb469-1" tabindex="-1"></a>cox_u1 <span class="ot">&lt;-</span> <span class="fu">update</span>(cox_u0, <span class="fu">update</span>(</span>
<span id="cb469-2"><a href="#cb469-2" tabindex="-1"></a>  fml_tenure_inf, . <span class="sc">~</span> fm_gender <span class="sc">+</span> cri_avgsd5_roll <span class="sc">+</span> gender_unemp <span class="sc">+</span> .</span>
<span id="cb469-3"><a href="#cb469-3" tabindex="-1"></a>))</span></code></pre></div>
<pre><code>## filter: removed 55,896 rows (56%), 44,616 rows remaining
## filter: removed 55,896 rows (56%), 44,616 rows remaining</code></pre>
<div class="sourceCode" id="cb471"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb471-1"><a href="#cb471-1" tabindex="-1"></a>model_list_tblB9 <span class="ot">&lt;-</span> <span class="fu">list</span>(cox_c0, cox_c1, cox_i0, cox_i1, cox_u0, cox_u1)</span>
<span id="cb471-2"><a href="#cb471-2" tabindex="-1"></a>model_list_tblB9 <span class="ot">&lt;-</span> <span class="fu">lapply</span>(model_list_tblB9, add_AIC)</span></code></pre></div>
<div class="sourceCode" id="cb472"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb472-1"><a href="#cb472-1" tabindex="-1"></a><span class="fu">stargazer</span>(model_list_tblB9,</span>
<span id="cb472-2"><a href="#cb472-2" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">lapply</span>(model_list_tblB9, get_clse_hr),</span>
<span id="cb472-3"><a href="#cb472-3" tabindex="-1"></a>          <span class="at">omit.stat =</span> <span class="fu">c</span>(<span class="st">&quot;rsq&quot;</span>, <span class="st">&quot;max.rsq&quot;</span>, <span class="st">&quot;wald&quot;</span>, <span class="st">&quot;lr&quot;</span>, <span class="st">&quot;logrank&quot;</span>),</span>
<span id="cb472-4"><a href="#cb472-4" tabindex="-1"></a>          <span class="at">covariate.labels =</span> <span class="fu">c</span>(<span class="st">&quot;Woman Minister&quot;</span>,</span>
<span id="cb472-5"><a href="#cb472-5" tabindex="-1"></a>                               <span class="st">&quot;Banking Crisis&quot;</span>, <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb472-6"><a href="#cb472-6" tabindex="-1"></a>                               <span class="st">&quot;Currency Crisis&quot;</span>, <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb472-7"><a href="#cb472-7" tabindex="-1"></a>                               <span class="st">&quot;Inflation Crisis&quot;</span>, <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb472-8"><a href="#cb472-8" tabindex="-1"></a>                               <span class="st">&quot;Unemployment Crisis&quot;</span>, <span class="st">&quot;Crisis x Woman&quot;</span>,</span>
<span id="cb472-9"><a href="#cb472-9" tabindex="-1"></a>                               <span class="st">&quot;Women in Parliament&quot;</span>,</span>
<span id="cb472-10"><a href="#cb472-10" tabindex="-1"></a>                               <span class="st">&quot;Woman Head of Government&quot;</span>, <span class="st">&quot;Presidential&quot;</span>,</span>
<span id="cb472-11"><a href="#cb472-11" tabindex="-1"></a>                               <span class="st">&quot;Democracy Score&quot;</span>, <span class="st">&quot;Unified Government&quot;</span>,</span>
<span id="cb472-12"><a href="#cb472-12" tabindex="-1"></a>                               <span class="st">&quot;Economic Growth&quot;</span>, <span class="st">&quot;Inflation&quot;</span>, <span class="st">&quot;GDP per Capita&quot;</span>,</span>
<span id="cb472-13"><a href="#cb472-13" tabindex="-1"></a>                               <span class="st">&quot;Trade Openness&quot;</span>, <span class="st">&quot;Capital Openness&quot;</span></span>
<span id="cb472-14"><a href="#cb472-14" tabindex="-1"></a>                               ),</span>
<span id="cb472-15"><a href="#cb472-15" tabindex="-1"></a>          <span class="at">dep.var.caption =</span> <span class="fu">c</span>(<span class="st">&quot;Hazard Rate (&gt; 0 means shorter duration)&quot;</span>),</span>
<span id="cb472-16"><a href="#cb472-16" tabindex="-1"></a>          <span class="at">dep.var.labels.include =</span> <span class="cn">FALSE</span>,</span>
<span id="cb472-17"><a href="#cb472-17" tabindex="-1"></a>          <span class="at">title =</span> <span class="st">&quot;Table B9: Tenure Model Robustness Checks, Currency, Inflation, and Unemployment Crises&quot;</span>,</span>
<span id="cb472-18"><a href="#cb472-18" tabindex="-1"></a>          <span class="at">type =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb472-19"><a href="#cb472-19" tabindex="-1"></a>          <span class="at">out =</span> <span class="st">&quot;Tables/tableB9.html&quot;</span>)</span></code></pre></div>
<table style="text-align:center">
<caption>
<strong>Table B9: Tenure Model Robustness Checks, Currency, Inflation,
and Unemployment Crises</strong>
</caption>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td colspan="6">
Hazard Rate (&gt; 0 means shorter duration)
</td>
</tr>
<tr>
<td>
</td>
<td colspan="6" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(1)
</td>
<td>
(2)
</td>
<td>
(3)
</td>
<td>
(4)
</td>
<td>
(5)
</td>
<td>
(6)
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Minister
</td>
<td>
0.129
</td>
<td>
0.110
</td>
<td>
0.130
</td>
<td>
0.062
</td>
<td>
0.198
</td>
<td>
0.140
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.124)
</td>
<td>
(0.112)
</td>
<td>
(0.124)
</td>
<td>
(0.118)
</td>
<td>
(0.138)
</td>
<td>
(0.130)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Banking Crisis
</td>
<td>
0.346<sup>***</sup>
</td>
<td>
</td>
<td>
0.350<sup>***</sup>
</td>
<td>
</td>
<td>
0.410<sup>***</sup>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.082)
</td>
<td>
</td>
<td>
(0.082)
</td>
<td>
</td>
<td>
(0.111)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
-0.480<sup>*</sup>
</td>
<td>
</td>
<td>
-0.482<sup>*</sup>
</td>
<td>
</td>
<td>
-0.699<sup>*</sup>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.252)
</td>
<td>
</td>
<td>
(0.253)
</td>
<td>
</td>
<td>
(0.359)
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Currency Crisis
</td>
<td>
</td>
<td>
0.300<sup>**</sup>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.117)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
</td>
<td>
-1.087
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
(0.998)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation Crisis
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.134<sup>**</sup>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.061)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.238
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.326)
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unemployment Crisis
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.077
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.111)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Crisis x Woman
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
0.006
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
(0.452)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Women in Parliament
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.015<sup>***</sup>
</td>
<td>
-0.017<sup>***</sup>
</td>
<td>
-0.017<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.002)
</td>
<td>
(0.003)
</td>
<td>
(0.003)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Woman Head of Government
</td>
<td>
-0.056
</td>
<td>
-0.054
</td>
<td>
-0.053
</td>
<td>
-0.045
</td>
<td>
-0.092
</td>
<td>
-0.106
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.138)
</td>
<td>
(0.136)
</td>
<td>
(0.137)
</td>
<td>
(0.136)
</td>
<td>
(0.172)
</td>
<td>
(0.171)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Presidential
</td>
<td>
0.115<sup>**</sup>
</td>
<td>
0.116<sup>**</sup>
</td>
<td>
0.115<sup>**</sup>
</td>
<td>
0.112<sup>**</sup>
</td>
<td>
-0.004
</td>
<td>
0.008
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
<td>
(0.049)
</td>
<td>
(0.070)
</td>
<td>
(0.070)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Democracy Score
</td>
<td>
0.212<sup>***</sup>
</td>
<td>
0.214<sup>***</sup>
</td>
<td>
0.211<sup>***</sup>
</td>
<td>
0.219<sup>***</sup>
</td>
<td>
0.147<sup>***</sup>
</td>
<td>
0.162<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
<td>
(0.037)
</td>
<td>
(0.054)
</td>
<td>
(0.054)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Unified Government
</td>
<td>
-0.045
</td>
<td>
-0.048
</td>
<td>
-0.047
</td>
<td>
-0.049
</td>
<td>
-0.043
</td>
<td>
-0.039
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
<td>
(0.046)
</td>
<td>
(0.062)
</td>
<td>
(0.062)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Economic Growth
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.023<sup>***</sup>
</td>
<td>
-0.021<sup>***</sup>
</td>
<td>
-0.022<sup>***</sup>
</td>
<td>
-0.025<sup>***</sup>
</td>
<td>
-0.029<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
<td>
(0.005)
</td>
<td>
(0.008)
</td>
<td>
(0.008)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Inflation
</td>
<td>
0.011
</td>
<td>
0.018
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.018)
</td>
<td>
(0.018)
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
GDP per Capita
</td>
<td>
-0.039
</td>
<td>
-0.037
</td>
<td>
-0.037
</td>
<td>
-0.040
</td>
<td>
-0.030
</td>
<td>
-0.023
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
<td>
(0.036)
</td>
<td>
(0.036)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Trade Openness
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.003<sup>***</sup>
</td>
<td>
-0.002<sup>***</sup>
</td>
<td>
-0.002<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
<td>
(0.0005)
</td>
<td>
(0.001)
</td>
<td>
(0.001)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td style="text-align:left">
Capital Openness
</td>
<td>
-0.017
</td>
<td>
-0.014
</td>
<td>
-0.018
</td>
<td>
-0.008
</td>
<td>
0.003
</td>
<td>
0.002
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
<td>
(0.019)
</td>
<td>
(0.027)
</td>
<td>
(0.027)
</td>
</tr>
<tr>
<td style="text-align:left">
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
Observations
</td>
<td>
64,057
</td>
<td>
64,057
</td>
<td>
64,093
</td>
<td>
64,069
</td>
<td>
36,365
</td>
<td>
36,365
</td>
</tr>
<tr>
<td style="text-align:left">
Log Likelihood
</td>
<td>
-13,747.280
</td>
<td>
-13,752.430
</td>
<td>
-13,749.220
</td>
<td>
-13,754.080
</td>
<td>
-7,061.707
</td>
<td>
-7,068.288
</td>
</tr>
<tr>
<td style="text-align:left">
Akaike Inf. Crit.
</td>
<td>
27,520.550
</td>
<td>
27,530.860
</td>
<td>
27,522.450
</td>
<td>
27,532.160
</td>
<td>
14,147.410
</td>
<td>
14,160.580
</td>
</tr>
<tr>
<td colspan="7" style="border-bottom: 1px solid black">
</td>
</tr>
<tr>
<td style="text-align:left">
<em>Note:</em>
</td>
<td colspan="6" style="text-align:right">
<sup><em></sup>p&lt;0.1; <sup><strong></sup>p&lt;0.05;
<sup></strong></em></sup>p&lt;0.01
</td>
</tr>
</table>
<p>Calculate N for column 3</p>
<div class="sourceCode" id="cb473"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb473-1"><a href="#cb473-1" tabindex="-1"></a>calc_c3 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
<span id="cb473-2"><a href="#cb473-2" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb473-3"><a href="#cb473-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb473-4"><a href="#cb473-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> </span>
<span id="cb473-5"><a href="#cb473-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb473-6"><a href="#cb473-6" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb473-7"><a href="#cb473-7" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb473-8"><a href="#cb473-8" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb473-9"><a href="#cb473-9" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 36,419 rows (36%), 64,093 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,287 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb477"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb477-1"><a href="#cb477-1" tabindex="-1"></a><span class="fu">table</span>(calc_c3 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  209 2078</code></pre>
<p>Calculate N for column 4</p>
<div class="sourceCode" id="cb479"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb479-1"><a href="#cb479-1" tabindex="-1"></a>calc_c4 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
<span id="cb479-2"><a href="#cb479-2" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb479-3"><a href="#cb479-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb479-4"><a href="#cb479-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(crisis20) <span class="sc">&amp;</span> </span>
<span id="cb479-5"><a href="#cb479-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb479-6"><a href="#cb479-6" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb479-7"><a href="#cb479-7" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb479-8"><a href="#cb479-8" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb479-9"><a href="#cb479-9" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 36,443 rows (36%), 64,069 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 2,287 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb483"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb483-1"><a href="#cb483-1" tabindex="-1"></a><span class="fu">table</span>(calc_c4 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  209 2078</code></pre>
<p>Calculate N for columns 5 and 6</p>
<div class="sourceCode" id="cb485"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb485-1"><a href="#cb485-1" tabindex="-1"></a>calc_c5 <span class="ot">&lt;-</span> df <span class="sc">|&gt;</span> <span class="fu">filter</span>(tenure_st <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
<span id="cb485-2"><a href="#cb485-2" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(womenrep_lag) <span class="sc">&amp;</span> </span>
<span id="cb485-3"><a href="#cb485-3" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(presidential) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(xm_qudsest) <span class="sc">&amp;</span> </span>
<span id="cb485-4"><a href="#cb485-4" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(unified_aut) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(cri_avgsd5_roll) <span class="sc">&amp;</span> </span>
<span id="cb485-5"><a href="#cb485-5" tabindex="-1"></a>                           <span class="sc">!</span><span class="fu">is.na</span>(tradepergdp_i_lag) <span class="sc">&amp;</span> <span class="sc">!</span><span class="fu">is.na</span>(kaopen_i_lag)) <span class="sc">|&gt;</span> </span>
<span id="cb485-6"><a href="#cb485-6" tabindex="-1"></a>  <span class="fu">group_by</span>(spellid) <span class="sc">|&gt;</span> </span>
<span id="cb485-7"><a href="#cb485-7" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">fail =</span> <span class="fu">max</span>(tenure_d),</span>
<span id="cb485-8"><a href="#cb485-8" tabindex="-1"></a>            <span class="at">right_cens_max =</span> <span class="fu">max</span>(right_cens)</span>
<span id="cb485-9"><a href="#cb485-9" tabindex="-1"></a>            )</span></code></pre></div>
<pre><code>## filter: removed 64,147 rows (64%), 36,365 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (spellid)</code></pre>
<pre><code>## summarise: now 1,331 rows and 3 columns, ungrouped</code></pre>
<div class="sourceCode" id="cb489"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb489-1"><a href="#cb489-1" tabindex="-1"></a><span class="fu">table</span>(calc_c5 <span class="sc">$</span> fail)</span></code></pre></div>
<pre><code>## 
##    0    1 
##  168 1163</code></pre>
</div>
</div>
<div id="appendix-c" class="section level1">
<h1>4. Appendix C</h1>
<div id="table-c1" class="section level2">
<h2>Table C1</h2>
<p>Gender</p>
<div class="sourceCode" id="cb491"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb491-1"><a href="#cb491-1" tabindex="-1"></a><span class="fu">table</span>(df_appC <span class="sc">$</span> ministergender, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>)</span></code></pre></div>
<pre><code>## 
##    0    1 
## 3141  123</code></pre>
<p>Advanced Economics Education</p>
<div class="sourceCode" id="cb493"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb493-1"><a href="#cb493-1" tabindex="-1"></a><span class="fu">with</span>(df_appC, <span class="fu">table</span>(advanced_econ_degree, ministergender, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##                     ministergender
## advanced_econ_degree    0    1
##                 0    1492   48
##                 1     918   67
##                 &lt;NA&gt;  731    8</code></pre>
<p>Advanced Economics Education</p>
<div class="sourceCode" id="cb495"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb495-1"><a href="#cb495-1" tabindex="-1"></a><span class="fu">with</span>(df_appC, <span class="fu">table</span>(phd_econ, ministergender, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##         ministergender
## phd_econ    0    1
##     0    1945   79
##     1     465   36
##     &lt;NA&gt;  731    8</code></pre>
<p>Technocrats</p>
<div class="sourceCode" id="cb497"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb497-1"><a href="#cb497-1" tabindex="-1"></a><span class="fu">with</span>(df_appC, <span class="fu">table</span>(technocrats, ministergender, <span class="at">useNA =</span> <span class="st">&quot;ifany&quot;</span>))</span></code></pre></div>
<pre><code>##            ministergender
## technocrats    0    1
##        0    1608   44
##        1     981   74
##        &lt;NA&gt;  552    5</code></pre>
</div>
<div id="table-c2" class="section level2">
<h2>Table C2</h2>
<p>Test whether a minister was a technocrat prior to serving as a
finance minister</p>
<div class="sourceCode" id="cb499"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb499-1"><a href="#cb499-1" tabindex="-1"></a><span class="do">## by gender</span></span>
<span id="cb499-2"><a href="#cb499-2" tabindex="-1"></a>tech_1 <span class="ot">&lt;-</span>  df_appC <span class="sc">|&gt;</span> </span>
<span id="cb499-3"><a href="#cb499-3" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(technocrats)) <span class="sc">|&gt;</span> </span>
<span id="cb499-4"><a href="#cb499-4" tabindex="-1"></a>  <span class="fu">group_by</span>(ministergender) <span class="sc">|&gt;</span> </span>
<span id="cb499-5"><a href="#cb499-5" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_obs =</span> <span class="fu">n</span>(),</span>
<span id="cb499-6"><a href="#cb499-6" tabindex="-1"></a>            <span class="at">mean =</span> <span class="fu">mean</span>(technocrats),</span>
<span id="cb499-7"><a href="#cb499-7" tabindex="-1"></a>            <span class="at">se =</span> <span class="fu">sd</span>(technocrats)<span class="sc">/</span><span class="fu">sqrt</span>(n_obs)) <span class="sc">|&gt;</span> </span>
<span id="cb499-8"><a href="#cb499-8" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">ministergender =</span> <span class="fu">if_else</span>(ministergender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women&quot;</span>, <span class="st">&quot;Men&quot;</span>))</span></code></pre></div>
<pre><code>## filter: removed 557 rows (17%), 2,707 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (ministergender)</code></pre>
<pre><code>## summarise: now 2 rows and 4 columns, ungrouped</code></pre>
<pre><code>## mutate: converted &#39;ministergender&#39; from double to character (0 new NA)</code></pre>
<div class="sourceCode" id="cb504"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb504-1"><a href="#cb504-1" tabindex="-1"></a><span class="do">## combined</span></span>
<span id="cb504-2"><a href="#cb504-2" tabindex="-1"></a>tech_2 <span class="ot">&lt;-</span> df_appC <span class="sc">|&gt;</span> </span>
<span id="cb504-3"><a href="#cb504-3" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(technocrats)) <span class="sc">|&gt;</span> </span>
<span id="cb504-4"><a href="#cb504-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_obs =</span> <span class="fu">n</span>(), </span>
<span id="cb504-5"><a href="#cb504-5" tabindex="-1"></a>            <span class="at">mean =</span> <span class="fu">mean</span>(technocrats),</span>
<span id="cb504-6"><a href="#cb504-6" tabindex="-1"></a>            <span class="at">se =</span> <span class="fu">sd</span>(technocrats)<span class="sc">/</span><span class="fu">sqrt</span>(n_obs)) <span class="sc">|&gt;</span> </span>
<span id="cb504-7"><a href="#cb504-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">ministergender =</span> <span class="st">&quot;Combined&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb504-8"><a href="#cb504-8" tabindex="-1"></a>  <span class="fu">select</span>(ministergender, n_obs, mean, se)</span></code></pre></div>
<pre><code>## filter: removed 557 rows (17%), 2,707 rows remaining</code></pre>
<pre><code>## summarise: now one row and 3 columns, ungrouped</code></pre>
<pre><code>## mutate: new variable &#39;ministergender&#39; (character) with one unique value and 0% NA</code></pre>
<pre><code>## select: columns reordered (ministergender, n_obs, mean, se)</code></pre>
<div class="sourceCode" id="cb509"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb509-1"><a href="#cb509-1" tabindex="-1"></a><span class="fu">rbind</span>(tech_1, tech_2)</span></code></pre></div>
<pre><code>## # A tibble: 3 × 4
##   ministergender n_obs  mean      se
##   &lt;chr&gt;          &lt;int&gt; &lt;dbl&gt;   &lt;dbl&gt;
## 1 Men             2589 0.379 0.00954
## 2 Women            118 0.627 0.0447 
## 3 Combined        2707 0.390 0.00938</code></pre>
<p>T-test</p>
<div class="sourceCode" id="cb511"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb511-1"><a href="#cb511-1" tabindex="-1"></a><span class="fu">t.test</span>(technocrats <span class="sc">~</span> ministergender, <span class="at">var.equal =</span> <span class="cn">TRUE</span>, <span class="at">data =</span> df_appC)</span></code></pre></div>
<pre><code>## 
##  Two Sample t-test
## 
## data:  technocrats by ministergender
## t = -5.4342, df = 2705, p-value = 5.996e-08
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.3377702 -0.1586455
## sample estimates:
## mean in group 0 mean in group 1 
##       0.3789108       0.6271186</code></pre>
<p>We could get equivalent results using linear regression.</p>
<div class="sourceCode" id="cb513"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb513-1"><a href="#cb513-1" tabindex="-1"></a><span class="fu">lm</span>(technocrats <span class="sc">~</span> ministergender, <span class="at">data =</span> df_appC) <span class="sc">|&gt;</span> <span class="fu">summary</span>()</span></code></pre></div>
<pre><code>## 
## Call:
## lm(formula = technocrats ~ ministergender, data = df_appC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6271 -0.3789 -0.3789  0.6211  0.6211 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.378911   0.009536  39.734   &lt;2e-16 ***
## ministergender 0.248208   0.045675   5.434    6e-08 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.4852 on 2705 degrees of freedom
##   (557 observations deleted due to missingness)
## Multiple R-squared:  0.0108, Adjusted R-squared:  0.01043 
## F-statistic: 29.53 on 1 and 2705 DF,  p-value: 5.996e-08</code></pre>
</div>
<div id="table-c3" class="section level2">
<h2>Table C3</h2>
<p>Test whether a minister has an advanced degree in economics</p>
<div class="sourceCode" id="cb515"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb515-1"><a href="#cb515-1" tabindex="-1"></a><span class="do">## by gender</span></span>
<span id="cb515-2"><a href="#cb515-2" tabindex="-1"></a>adve_1 <span class="ot">&lt;-</span>  df_appC <span class="sc">|&gt;</span> </span>
<span id="cb515-3"><a href="#cb515-3" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(advanced_econ_degree)) <span class="sc">|&gt;</span> </span>
<span id="cb515-4"><a href="#cb515-4" tabindex="-1"></a>  <span class="fu">group_by</span>(ministergender) <span class="sc">|&gt;</span> </span>
<span id="cb515-5"><a href="#cb515-5" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_obs =</span> <span class="fu">n</span>(),</span>
<span id="cb515-6"><a href="#cb515-6" tabindex="-1"></a>            <span class="at">mean =</span> <span class="fu">mean</span>(advanced_econ_degree),</span>
<span id="cb515-7"><a href="#cb515-7" tabindex="-1"></a>            <span class="at">se =</span> <span class="fu">sd</span>(advanced_econ_degree)<span class="sc">/</span><span class="fu">sqrt</span>(n_obs)) <span class="sc">|&gt;</span> </span>
<span id="cb515-8"><a href="#cb515-8" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">ministergender =</span> <span class="fu">if_else</span>(ministergender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women&quot;</span>, <span class="st">&quot;Men&quot;</span>))</span></code></pre></div>
<pre><code>## filter: removed 739 rows (23%), 2,525 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (ministergender)</code></pre>
<pre><code>## summarise: now 2 rows and 4 columns, ungrouped</code></pre>
<pre><code>## mutate: converted &#39;ministergender&#39; from double to character (0 new NA)</code></pre>
<div class="sourceCode" id="cb520"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb520-1"><a href="#cb520-1" tabindex="-1"></a><span class="do">## combined</span></span>
<span id="cb520-2"><a href="#cb520-2" tabindex="-1"></a>adve_2 <span class="ot">&lt;-</span> df_appC <span class="sc">|&gt;</span> </span>
<span id="cb520-3"><a href="#cb520-3" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(advanced_econ_degree)) <span class="sc">|&gt;</span> </span>
<span id="cb520-4"><a href="#cb520-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_obs =</span> <span class="fu">n</span>(), </span>
<span id="cb520-5"><a href="#cb520-5" tabindex="-1"></a>            <span class="at">mean =</span> <span class="fu">mean</span>(advanced_econ_degree),</span>
<span id="cb520-6"><a href="#cb520-6" tabindex="-1"></a>            <span class="at">se =</span> <span class="fu">sd</span>(advanced_econ_degree)<span class="sc">/</span><span class="fu">sqrt</span>(n_obs)) <span class="sc">|&gt;</span> </span>
<span id="cb520-7"><a href="#cb520-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">ministergender =</span> <span class="st">&quot;Combined&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb520-8"><a href="#cb520-8" tabindex="-1"></a>  <span class="fu">select</span>(ministergender, n_obs, mean, se)</span></code></pre></div>
<pre><code>## filter: removed 739 rows (23%), 2,525 rows remaining</code></pre>
<pre><code>## summarise: now one row and 3 columns, ungrouped</code></pre>
<pre><code>## mutate: new variable &#39;ministergender&#39; (character) with one unique value and 0% NA</code></pre>
<pre><code>## select: columns reordered (ministergender, n_obs, mean, se)</code></pre>
<div class="sourceCode" id="cb525"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb525-1"><a href="#cb525-1" tabindex="-1"></a><span class="fu">rbind</span>(adve_1, adve_2)</span></code></pre></div>
<pre><code>## # A tibble: 3 × 4
##   ministergender n_obs  mean      se
##   &lt;chr&gt;          &lt;int&gt; &lt;dbl&gt;   &lt;dbl&gt;
## 1 Men             2410 0.381 0.00989
## 2 Women            115 0.583 0.0462 
## 3 Combined        2525 0.390 0.00971</code></pre>
<p>T-test</p>
<div class="sourceCode" id="cb527"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb527-1"><a href="#cb527-1" tabindex="-1"></a><span class="fu">t.test</span>(advanced_econ_degree <span class="sc">~</span> ministergender, <span class="at">var.equal =</span> <span class="cn">TRUE</span>, <span class="at">data =</span> df_appC)</span></code></pre></div>
<pre><code>## 
##  Two Sample t-test
## 
## data:  advanced_econ_degree by ministergender
## t = -4.3466, df = 2523, p-value = 1.437e-05
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.2926869 -0.1107047
## sample estimates:
## mean in group 0 mean in group 1 
##       0.3809129       0.5826087</code></pre>
<p>We could get equivalent results using linear regression.</p>
<div class="sourceCode" id="cb529"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb529-1"><a href="#cb529-1" tabindex="-1"></a><span class="fu">lm</span>(advanced_econ_degree <span class="sc">~</span> ministergender, <span class="at">data =</span> df_appC) <span class="sc">|&gt;</span> <span class="fu">summary</span>()</span></code></pre></div>
<pre><code>## 
## Call:
## lm(formula = advanced_econ_degree ~ ministergender, data = df_appC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5826 -0.3809 -0.3809  0.6191  0.6191 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.380913   0.009903  38.465  &lt; 2e-16 ***
## ministergender 0.201696   0.046403   4.347 1.44e-05 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.4861 on 2523 degrees of freedom
##   (739 observations deleted due to missingness)
## Multiple R-squared:  0.007433,   Adjusted R-squared:  0.007039 
## F-statistic: 18.89 on 1 and 2523 DF,  p-value: 1.437e-05</code></pre>
</div>
<div id="table-c4" class="section level2">
<h2>Table C4</h2>
<p>Test whether a minister has a Ph.D. in economics</p>
<div class="sourceCode" id="cb531"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb531-1"><a href="#cb531-1" tabindex="-1"></a><span class="do">## by gender</span></span>
<span id="cb531-2"><a href="#cb531-2" tabindex="-1"></a>phd_1 <span class="ot">&lt;-</span>  df_appC <span class="sc">|&gt;</span> </span>
<span id="cb531-3"><a href="#cb531-3" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(phd_econ)) <span class="sc">|&gt;</span> </span>
<span id="cb531-4"><a href="#cb531-4" tabindex="-1"></a>  <span class="fu">group_by</span>(ministergender) <span class="sc">|&gt;</span> </span>
<span id="cb531-5"><a href="#cb531-5" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_obs =</span> <span class="fu">n</span>(),</span>
<span id="cb531-6"><a href="#cb531-6" tabindex="-1"></a>            <span class="at">mean =</span> <span class="fu">mean</span>(phd_econ),</span>
<span id="cb531-7"><a href="#cb531-7" tabindex="-1"></a>            <span class="at">se =</span> <span class="fu">sd</span>(phd_econ)<span class="sc">/</span><span class="fu">sqrt</span>(n_obs)) <span class="sc">|&gt;</span> </span>
<span id="cb531-8"><a href="#cb531-8" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">ministergender =</span> <span class="fu">if_else</span>(ministergender <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Women&quot;</span>, <span class="st">&quot;Men&quot;</span>))</span></code></pre></div>
<pre><code>## filter: removed 739 rows (23%), 2,525 rows remaining</code></pre>
<pre><code>## group_by: one grouping variable (ministergender)</code></pre>
<pre><code>## summarise: now 2 rows and 4 columns, ungrouped</code></pre>
<pre><code>## mutate: converted &#39;ministergender&#39; from double to character (0 new NA)</code></pre>
<div class="sourceCode" id="cb536"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb536-1"><a href="#cb536-1" tabindex="-1"></a><span class="do">## combined</span></span>
<span id="cb536-2"><a href="#cb536-2" tabindex="-1"></a>phd_2 <span class="ot">&lt;-</span> df_appC <span class="sc">|&gt;</span> </span>
<span id="cb536-3"><a href="#cb536-3" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(phd_econ)) <span class="sc">|&gt;</span> </span>
<span id="cb536-4"><a href="#cb536-4" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n_obs =</span> <span class="fu">n</span>(), </span>
<span id="cb536-5"><a href="#cb536-5" tabindex="-1"></a>            <span class="at">mean =</span> <span class="fu">mean</span>(phd_econ),</span>
<span id="cb536-6"><a href="#cb536-6" tabindex="-1"></a>            <span class="at">se =</span> <span class="fu">sd</span>(phd_econ)<span class="sc">/</span><span class="fu">sqrt</span>(n_obs)) <span class="sc">|&gt;</span> </span>
<span id="cb536-7"><a href="#cb536-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">ministergender =</span> <span class="st">&quot;Combined&quot;</span>) <span class="sc">|&gt;</span> </span>
<span id="cb536-8"><a href="#cb536-8" tabindex="-1"></a>  <span class="fu">select</span>(ministergender, n_obs, mean, se)</span></code></pre></div>
<pre><code>## filter: removed 739 rows (23%), 2,525 rows remaining</code></pre>
<pre><code>## summarise: now one row and 3 columns, ungrouped</code></pre>
<pre><code>## mutate: new variable &#39;ministergender&#39; (character) with one unique value and 0% NA</code></pre>
<pre><code>## select: columns reordered (ministergender, n_obs, mean, se)</code></pre>
<div class="sourceCode" id="cb541"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb541-1"><a href="#cb541-1" tabindex="-1"></a><span class="fu">rbind</span>(phd_1, phd_2)</span></code></pre></div>
<pre><code>## # A tibble: 3 × 4
##   ministergender n_obs  mean      se
##   &lt;chr&gt;          &lt;int&gt; &lt;dbl&gt;   &lt;dbl&gt;
## 1 Men             2410 0.193 0.00804
## 2 Women            115 0.313 0.0434 
## 3 Combined        2525 0.198 0.00794</code></pre>
<p>T-test</p>
<div class="sourceCode" id="cb543"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb543-1"><a href="#cb543-1" tabindex="-1"></a><span class="fu">t.test</span>(phd_econ <span class="sc">~</span> ministergender, <span class="at">var.equal =</span> <span class="cn">TRUE</span>, <span class="at">data =</span> df_appC)</span></code></pre></div>
<pre><code>## 
##  Two Sample t-test
## 
## data:  phd_econ by ministergender
## t = -3.16, df = 2523, p-value = 0.001597
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.19462325 -0.04557159
## sample estimates:
## mean in group 0 mean in group 1 
##       0.1929461       0.3130435</code></pre>
<p>We could get equivalent results using linear regression.</p>
<div class="sourceCode" id="cb545"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb545-1"><a href="#cb545-1" tabindex="-1"></a><span class="fu">lm</span>(phd_econ <span class="sc">~</span> ministergender, <span class="at">data =</span> df_appC) <span class="sc">|&gt;</span> <span class="fu">summary</span>()</span></code></pre></div>
<pre><code>## 
## Call:
## lm(formula = phd_econ ~ ministergender, data = df_appC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3130 -0.1930 -0.1930 -0.1930  0.8071 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.192946   0.008111   23.79   &lt;2e-16 ***
## ministergender 0.120097   0.038006    3.16   0.0016 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3982 on 2523 degrees of freedom
##   (739 observations deleted due to missingness)
## Multiple R-squared:  0.003942,   Adjusted R-squared:  0.003547 
## F-statistic: 9.985 on 1 and 2523 DF,  p-value: 0.001597</code></pre>
</div>
</div>
<div id="session-information" class="section level1">
<h1>Session Information</h1>
<details>
<summary>
Session Info
</summary>
<div class="sourceCode" id="cb547"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb547-1"><a href="#cb547-1" tabindex="-1"></a><span class="fu">Sys.time</span>()</span></code></pre></div>
<pre><code>## [1] &quot;2023-06-22 21:21:56 CST&quot;</code></pre>
<div class="sourceCode" id="cb549"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb549-1"><a href="#cb549-1" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span></code></pre></div>
<pre><code>## R version 4.3.0 (2023-04-21)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Asia/Shanghai
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] patchwork_1.1.2    gghalves_0.1.4     ggdist_3.3.0       rcartocolor_2.1.1 
##  [5] kableExtra_1.3.4   knitr_1.43         survival_3.5-5     lmtest_0.9-40     
##  [9] zoo_1.8-12         multiwayvcov_1.2.3 stargazer_5.2.3    pltesim_1.0       
## [13] countrycode_1.5.0  maps_3.4.1         DT_0.28            tidylog_1.0.2     
## [17] lubridate_1.9.2    forcats_1.0.0      stringr_1.5.0      dplyr_1.1.2       
## [21] purrr_1.0.1        readr_2.1.4        tidyr_1.3.0        tibble_3.2.1      
## [25] ggplot2_3.4.2      tidyverse_2.0.0   
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0     viridisLite_0.4.2    farver_2.1.1        
##  [4] fastmap_1.1.1        pacman_0.5.1         digest_0.6.31       
##  [7] timechange_0.2.0     lifecycle_1.0.3      ellipsis_0.3.2      
## [10] magrittr_2.0.3       compiler_4.3.0       rlang_1.1.1         
## [13] sass_0.4.6           tools_4.3.0          utf8_1.2.3          
## [16] yaml_2.3.7           labeling_0.4.2       htmlwidgets_1.6.2   
## [19] bit_4.0.5            xml2_1.3.4           withr_2.5.0         
## [22] grid_4.3.0           fansi_1.0.4          colorspace_2.1-0    
## [25] scales_1.2.1         MASS_7.3-60          cli_3.6.1           
## [28] rmarkdown_2.22       crayon_1.5.2         ragg_1.2.5          
## [31] generics_0.1.3       rstudioapi_0.14      httr_1.4.6          
## [34] tzdb_0.4.0           cachem_1.0.8         splines_4.3.0       
## [37] rvest_1.0.3          parallel_4.3.0       vctrs_0.6.3         
## [40] boot_1.3-28.1        webshot_0.5.4        Matrix_1.5-4        
## [43] sandwich_3.0-2       jsonlite_1.8.5       coreSim_0.2.4       
## [46] hms_1.1.3            bit64_4.0.5          archive_1.1.5       
## [49] crosstalk_1.2.0      systemfonts_1.0.4    jquerylib_0.1.4     
## [52] glue_1.6.2           distributional_0.3.2 stringi_1.7.12      
## [55] gtable_0.3.3         munsell_0.5.0        pillar_1.9.0        
## [58] clisymbols_1.2.0     htmltools_0.5.5      R6_2.5.1            
## [61] textshaping_0.3.6    vroom_1.6.3          evaluate_0.21       
## [64] lattice_0.21-8       highr_0.10           bslib_0.5.0         
## [67] svglite_2.1.1        xfun_0.39            pkgconfig_2.0.3</code></pre>
</details>
</div>



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